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One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations. [ABSTRACT FROM AUTHOR]

Simple Summary: Skin diseases can be serious, and early detection is key to effective treatment. Unfortunately, the quality of images used to diagnose these diseases often suffers due to interference from hair, making accurate diagnosis challenging. This research introduces a novel technology, the DM–AHR, a self-supervised conditional diffusion model designed specifically to generate clear, hairless images for better skin disease diagnosis. Our work not only presents a new, advanced model that expertly identifies and removes hair from dermoscopic images but also introduces a specialized dataset, DERMAHAIR, to further research and improve diagnostic processes. The enhancements in image quality provided by DM–AHR significantly improve the accuracy of skin disease diagnoses, and it promises to be a valuable tool in medical imaging. Accurate skin diagnosis through end-user applications is important for early detection and cure of severe skin diseases. However, the low quality of dermoscopic images hampers this mission, especially with the presence of hair on these kinds of images. This paper introduces DM–AHR, a novel, self-supervised conditional diffusion model designed specifically for the automatic generation of hairless dermoscopic images to improve the quality of skin diagnosis applications. The current research contributes in three significant ways to the field of dermatologic imaging. First, we develop a customized diffusion model that adeptly differentiates between hair and skin features. Second, we pioneer a novel self-supervised learning strategy that is specifically tailored to optimize performance for hairless imaging. Third, we introduce a new dataset, named DERMAHAIR (DERMatologic Automatic HAIR Removal Dataset), that is designed to advance and benchmark research in this specialized domain. These contributions significantly enhance the clarity of dermoscopic images, improving the accuracy of skin diagnosis procedures. We elaborate on the architecture of DM–AHR and demonstrate its effective performance in removing hair while preserving critical details of skin lesions. Our results show an enhancement in the accuracy of skin lesion analysis when compared to existing techniques. Given its robust performance, DM–AHR holds considerable promise for broader application in medical image enhancement. [ABSTRACT FROM AUTHOR]

Simple Summary: HER2-positive breast cancer occurs in 15–30% of cases and has a poor prognosis. Digital image analysis of HER2 is promising, but its implementation in real clinical practice remains unclear. This systematic review evaluates the effectiveness of digital image analysis algorithms for HER2 in breast cancer and their performance, with a focus on testing them in real-world clinical settings. The authors aim to assess the applicability of these algorithms in practical clinical scenarios. By analyzing 25 papers from the period 2013–2024 and emphasizing mostly deep learning approaches, the review underscores the importance of standardized evaluation criteria, study designs tailored for clinical applications, and clinical validation. While direct evidence of clinical application was not found, the findings aim to guide future research and the implementation of digital image analysis in breast cancer diagnosis within clinical settings, potentially impacting the research community by advancing algorithmic applications in real clinical practice. This systematic review aims to address the research gap in the performance of computational algorithms for the digital image analysis of HER2 images in clinical settings. While numerous studies have explored various aspects of these algorithms, there is a lack of comprehensive evaluation regarding their effectiveness in real-world clinical applications. We conducted a search of the Web of Science and PubMed databases for studies published from 31 December 2013 to 30 June 2024, focusing on performance effectiveness and components such as dataset size, diversity and source, ground truth, annotation, and validation methods. The study was registered with PROSPERO (CRD42024525404). Key questions guiding this review include the following: How effective are current computational algorithms at detecting HER2 status in digital images? What are the common validation methods and dataset characteristics used in these studies? Is there standardization of algorithm evaluations of clinical applications that can improve the clinical utility and reliability of computational tools for HER2 detection in digital image analysis? We identified 6833 publications, with 25 meeting the inclusion criteria. The accuracy rate with clinical datasets varied from 84.19% to 97.9%. The highest accuracy was achieved on the publicly available Warwick dataset at 98.8% in synthesized datasets. Only 12% of studies used separate datasets for external validation; 64% of studies used a combination of accuracy, precision, recall, and F1 as a set of performance measures. Despite the high accuracy rates reported in these studies, there is a notable absence of direct evidence supporting their clinical application. To facilitate the integration of these technologies into clinical practice, there is an urgent need to address real-world challenges and overreliance on internal validation. Standardizing study designs on real clinical datasets can enhance the reliability and clinical applicability of computational algorithms in improving the detection of HER2 cancer. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is a significant cause of female cancer-related deaths in the US. Checking how severe the cancer is helps in planning treatment. Modern AI methods are good at grading cancer, but they are not used much in hospitals yet. We developed and utilized ensemble deep learning algorithms for addressing the tasks of classifying (1) breast cancer subtype and (2) breast cancer invasiveness from whole slide image (WSI) histopathology slides. The ensemble models used were based on convolutional neural networks (CNNs) known for extracting distinctive features crucial for accurate classification. In this paper, we provide a comprehensive analysis of these models and the used methodology for breast cancer diagnosis tasks. Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens. [ABSTRACT FROM AUTHOR]

Simple Summary: The 2021 WHO classification of central nervous system (CNS) tumors is challenging for neuroradiologists due to the central role of the molecular profile of tumors. We performed a scoping review of recent literature to assess the existing data on the power of novel data analysis tools to predict new tumor classes by imaging. We found room for performance improvement for subgroups with lower incidence (e.g., 1p/19q codeleted or IDH1/2 mutated gliomas) and patients with rare diagnoses (e.g., pediatric gliomas, midline gliomas). More data regarding functional MRI techniques need to be collected. Studies explicitly designed to assess the generalizability of AI-aided tools for predicting molecular tumor subgroups are lacking. The 2021 WHO classification of CNS tumors is a challenge for neuroradiologists due to the central role of the molecular profile of tumors. The potential of novel data analysis tools in neuroimaging must be harnessed to maintain its role in predicting tumor subgroups. We performed a scoping review to determine current evidence and research gaps. A comprehensive literature search was conducted regarding glioma subgroups according to the 2021 WHO classification and the use of MRI, radiomics, machine learning, and deep learning algorithms. Sixty-two original articles were included and analyzed by extracting data on the study design and results. Only 8% of the studies included pediatric patients. Low-grade gliomas and diffuse midline gliomas were represented in one-third of the research papers. Public datasets were utilized in 22% of the studies. Conventional imaging sequences prevailed; data on functional MRI (DWI, PWI, CEST, etc.) are underrepresented. Multiparametric MRI yielded the best prediction results. IDH mutation and 1p/19q codeletion status prediction remain in focus with limited data on other molecular subgroups. Reported AUC values range from 0.6 to 0.98. Studies designed to assess generalizability are scarce. Performance is worse for smaller subgroups (e.g., 1p/19q codeleted or IDH1/2 mutated gliomas). More high-quality study designs with diversity in the analyzed population and techniques are needed. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper proposes a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN) and implemented on three publicly available benchmark datasets (ISIC 2020, HAM10000, and DermIS). The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17% accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The accuracies of ResNet-152, Vgg-19, MobileNet, and Vgg-16, EfficientNet-B0, and Inception-V3 are 89.68%, 92.51%, 91.46%, 89.12%, 89.46%, and 91.82%, respectively. The results showed that the proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer. Skin cancer is one of the most lethal kinds of human illness. In the present state of the health care system, skin cancer identification is a time-consuming procedure and if it is not diagnosed initially then it can be threatening to human life. To attain a high prospect of complete recovery, early detection of skin cancer is crucial. In the last several years, the application of deep learning (DL) algorithms for the detection of skin cancer has grown in popularity. Based on a DL model, this work intended to build a multi-classification technique for diagnosing skin cancers such as melanoma (MEL), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevi (MN). In this paper, we have proposed a novel model, a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN), and evaluated it on three publicly available benchmark datasets (i.e., ISIC 2020, HAM10000, and DermIS). For the skin cancer diagnosis, the classification performance of the proposed DSCC_Net model is compared with six baseline deep networks, including ResNet-152, Vgg-16, Vgg-19, Inception-V3, EfficientNet-B0, and MobileNet. In addition, we used SMOTE Tomek to handle the minority classes issue that exists in this dataset. The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17%, accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The rates of accuracy for ResNet-152, Vgg-19, MobileNet, Vgg-16, EfficientNet-B0, and Inception-V3 are 89.32%, 91.68%, 92.51%, 91.12%, 89.46% and 91.82%, respectively. The results showed that our proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer. [ABSTRACT FROM AUTHOR]

Simple Summary: According to cancer statistics published in 2020, there were 19.3 million new cancer cases and almost 10.0 million cancer deaths worldwide. This suggests that cancer is one of the main health threats worldwide. Survival analysis, combining the exponential distribution with regression analysis, can predict the time when a specific event will occur. This nationwide follow-up study aims to present survival deep learning models to assist in the early identification of cancer incidence. Our models consistently achieved high performance in 10 types of cancer. By applying the techniques outlined in this paper, clinical biomarkers, demographic, and anthropometric data can be utilized to predict the risk of cancer occurrence. Background: Cancer is one of the main global health threats. Early personalized prediction of cancer incidence is crucial for the population at risk. This study introduces a novel cancer prediction model based on modern recurrent survival deep learning algorithms. Methods: The study includes 160,407 participants from the blood-based cohort of the Korea Cancer Prevention Research-II Biobank, which has been ongoing since 2004. Data linkages were designed to ensure anonymity, and data collection was carried out through nationwide medical examinations. Predictive performance on ten cancer sites, evaluated using the concordance index (c-index), was compared among nDeep and its multitask variation, Cox proportional hazard (PH) regression, DeepSurv, and DeepHit. Results: Our models consistently achieved a c-index of over 0.8 for all ten cancers, with a peak of 0.8922 for lung cancer. They outperformed Cox PH regression and other survival deep neural networks. Conclusion: This study presents a survival deep learning model that demonstrates the highest predictive performance on censored health dataset, to the best of our knowledge. In the future, we plan to investigate the causal relationship between explanatory variables and cancer to reduce cancer incidence and mortality. [ABSTRACT FROM AUTHOR]

Simple Summary: Melanoma is the most dangerous type of skin cancer worldwide. Early detection of melanoma is crucial for better outcomes, but this often can be challenging. This research explores the use of artificial intelligence (AI) techniques combined with non-invasive imaging methods to improve melanoma detection. The authors aim to evaluate the current state of AI-based techniques using tools including dermoscopy, optical coherence tomography (OCT), and reflectance confocal microscopy (RCM). The findings demonstrate that several AI algorithms perform as well as or better than dermatologists in detecting melanoma, particularly in the analysis of dermoscopy images. This research highlights the potential of AI to enhance diagnostic accuracy, leading to improved patient outcomes. Further studies are needed to address limitations and ensure the reliability and effectiveness of AI-based techniques. Background: Melanoma, the deadliest form of skin cancer, poses a significant public health challenge worldwide. Early detection is crucial for improved patient outcomes. Non-invasive skin imaging techniques allow for improved diagnostic accuracy; however, their use is often limited due to the need for skilled practitioners trained to interpret images in a standardized fashion. Recent innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation show potential for the use of AI in the early detection of melanoma. Objective: The aim of this study was to evaluate the current state of AI-based techniques used in combination with non-invasive diagnostic imaging modalities including reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and dermoscopy. We also aimed to determine whether the application of AI-based techniques can lead to improved diagnostic accuracy of melanoma. Methods: A systematic search was conducted via the Medline/PubMed, Cochrane, and Embase databases for eligible publications between 2018 and 2022. Screening methods adhered to the 2020 version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Included studies utilized AI-based algorithms for melanoma detection and directly addressed the review objectives. Results: We retrieved 40 papers amongst the three databases. All studies directly comparing the performance of AI-based techniques with dermatologists reported the superior or equivalent performance of AI-based techniques in improving the detection of melanoma. In studies directly comparing algorithm performance on dermoscopy images to dermatologists, AI-based algorithms achieved a higher ROC (>80%) in the detection of melanoma. In these comparative studies using dermoscopic images, the mean algorithm sensitivity was 83.01% and the mean algorithm specificity was 85.58%. Studies evaluating machine learning in conjunction with OCT boasted accuracy of 95%, while studies evaluating RCM reported a mean accuracy rate of 82.72%. Conclusions: Our results demonstrate the robust potential of AI-based techniques to improve diagnostic accuracy and patient outcomes through the early identification of melanoma. Further studies are needed to assess the generalizability of these AI-based techniques across different populations and skin types, improve standardization in image processing, and further compare the performance of AI-based techniques with board-certified dermatologists to evaluate clinical applicability. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper presents a cervical cancer detection approach where the KNN Imputer techniques is used to fill the missing values and after that SMOTE upsampled features are utilized to train a multi-model ensemble learning approach. Results demonstrate that use of KNN Imputed SMOTE features yields better results than the original features to classify cancerous and normal patients. Objective: Cervical cancer ranks among the top causes of death among females in developing countries. The most important procedures that should be followed to guarantee the minimizing of cervical cancer's aftereffects are early identification and treatment under the finest medical guidance. One of the best methods to find this sort of malignancy is by looking at a Pap smear image. For automated detection of cervical cancer, the available datasets often have missing values, which can significantly affect the performance of machine learning models. Methods: To address these challenges, this study proposes an automated system for predicting cervical cancer that efficiently handles missing values with SMOTE features to achieve high accuracy. The proposed system employs a stacked ensemble voting classifier model that combines three machine learning models, along with KNN Imputer and SMOTE up-sampled features for handling missing values. Results: The proposed model achieves 99.99% accuracy, 99.99% precision, 99.99% recall, and 99.99% F1 score when using KNN imputed SMOTE features. The study compares the performance of the proposed model with multiple other machine learning algorithms under four scenarios: with missing values removed, with KNN imputation, with SMOTE features, and with KNN imputed SMOTE features. The study validates the efficacy of the proposed model against existing state-of-the-art approaches. Conclusions: This study investigates the issue of missing values and class imbalance in the data collected for cervical cancer detection and might aid medical practitioners in timely detection and providing cervical cancer patients with better care. [ABSTRACT FROM AUTHOR]

The present paper introduces a general multidimensional model to measure individual differences in learning within a single administration of a test. Learning is assumed to result from practicing the operations involved in solving the items. The model accounts for the possibility that the ability to learn may manifest differently for correct and incorrect responses, which allows for distinguishing different types of learning effects in the data. Model estimation and evaluation is based on a Bayesian framework. A simulation study is presented that examines the performance of the estimation and evaluation methods. The results show accuracy in parameter recovery as well as good performance in model evaluation and selection. An empirical study illustrates the applicability of the model to data from a logical ability test. [ABSTRACT FROM AUTHOR]

Predicting the polyproline type II (PPII) helix structure is crucial important in many research areas, such as the protein folding mechanisms, the drug targets, and the protein functions. However, many existing PPII helix prediction algorithms encode the protein sequence information in a single way, which causes the insufficient learning of protein sequence feature information. To improve the protein sequence encoding performance, this paper proposes a BERT-based PPII helix structure prediction algorithm (BERT-PPII), which learns the protein sequence information based on the BERT model. The BERT model's CLS vector can fairly fuse sample's each amino acid residue information. Thus, we utilize the CLS vector as the global feature to represent the sample's global contextual information. As the interactions among the protein chains' local amino acid residues have an important influence on the formation of PPII helix, we utilize the CNN to extract local amino acid residues' features which can further enhance the information expression of protein sequence samples. In this paper, we fuse the CLS vectors with CNN local features to improve the performance of predicting PPII structure. Compared to the state-of-the-art PPIIPRED method, the experimental results on the unbalanced dataset show that the proposed method improves the accuracy value by 1% on the strict dataset and 2% on the less strict dataset. Correspondingly, the results on the balanced dataset show that the AUCs of the proposed method are 0.826 on the strict dataset and 0.785 on less strict datasets, respectively. For the independent test set, the proposed method has the AUC value of 0.827 on the strict dataset and 0.783 on the less strict dataset. The above experimental results have proved that the proposed BERT-PPII method can achieve a superior performance of predicting the PPII helix. [ABSTRACT FROM AUTHOR]

In recent years, with the acceleration of urbanization and the implementation of compulsory education, the pressure on students' study and life has increased, and the phenomenon of psychological and behavioral problems has become increasingly prominent. Therefore, the school has regarded students' mental health education as the top priority in teaching work. Effective expression classification can assist psychology researchers to study psychology and other disciplines and analyze children's psychological activities and mental states by classifying expressions, thereby reducing the occurrence of psychological behavior problems. Most of the current mainstream methods focus on the exploration of text explicit features and the optimization of representation models, and few works pay attention to deeper language expressions. Metaphors, as language expressions often used in daily life, are closely related to an individual's emotion, cognition, and psychological state. This paper studies children's smiling face recognition based on deep neural network. In order to obtain a better identification effect of mental health problems of children, this paper attempts to use multisource data, including consumption data, access control data, network logs, and grade data, and proposes a multisource data-based mental health problem identification algorithm. The main research focus is feature extraction, trying to use one-dimensional convolutional neural network (1D-CNN) to mine students' online patterns from online behavior sequences, calculate abnormal scores based on students' consumption data in the cafeteria, and describe the dietary differences among students. At the same time, this paper uses the students' psychological state data provided by the psychological center as a label to improve the deficiencies caused by the questionnaire. This paper uses the training set to train five common classification algorithms, evaluates them through the validation set, and selects the best classifier as our algorithm and uses it to identify students with mental health problems in the test set. The experimental results show that precision reaches 0.68, recall reaches 0.56, and F 1 -measure reaches 0.67. [ABSTRACT FROM AUTHOR]

Traditional English classroom teaching is difficult to meet the oral learning needs of most learners. Thanks to the continuous advancement of speech processing technology, computer-assisted language learning systems are becoming more intelligent, not only pointing out learners' pronunciation errors but also assessing their overall pronunciation level. Method. This paper uses the method of tree kernel function to measure the similarity of two ontology trees. According to the features of nodes in ontology tree, methods to calculate the external features and internal features of nodes are proposed, respectively. External features are mainly obtained by calculating the hierarchical centrality, node density, and node coverage of nodes in the ontology tree; internal features are mainly obtained by measuring the richness of internal information. According to the similarity of ontology tree and the external features and internal features of nodes, the calculation formula of structural comprehensive similarity is improved, and the features of ontology itself can be fully considered in the calculation. According to the difference of the structure, the weights of the corresponding features during the calculation are adjusted autonomously, so that the calculation results are closer to reality. In spectral image preprocessing, endpoint detection utilizes the harmonic characteristics presented by narrowband spectrograms with high frequency resolution and eliminates useless nonspeech segments by detecting the presence of voiced segments. When building the neural network model, four convolutional layers, two fully connected layers, and one softmax output layer were conceived, and dropout was used to randomly suspend the work of some neurons to avoid overfitting. Results/Discussion. Through the data analysis of mean and variance and verified by one-way analysis of variance, it proves that the sentiment evaluation method in this paper is effective. The traditional multiple linear regression method is not suitable for the corpus and application scenarios of this paper. This paper proposes a decision tree structure, which is similar to the overall scoring process of raters, and uses the Interactive Dicremiser version 3 (ID3) algorithm to build a comprehensive evaluation decision tree for pitch, rhythm, intonation, speech rate, and emotion indicators. It is proved by experiments that the accurate consistency rate of the human-machine evaluation in this paper is 93%, the adjacent consistency rate is 96%, and the Pearson correlation coefficient value of the human-machine evaluation results is 0.89. The data results prove that the evaluation method in this paper is credible. [ABSTRACT FROM AUTHOR]

With the continuous updating and advancement of artificial intelligence technology, it gradually begins to shine in various industries, especially playing an increasingly important role in incremental music teaching and assisted therapy systems. This study designs artificial intelligence models from the perspectives of attention mechanism, contextual information guidance, and distant dependencies combined with incremental music teaching for the segmentation of MS (multiple sclerosis) lesions and achieves the automatic and accurate segmentation of MS lesions through the multidimensional analysis of multimodal magnetic resonance imaging data, which provides a basis for physicians to quantitatively analyze MS lesions, thus assisting them in the diagnosis and treatment of MS. To address the highly variable characteristics of MS lesion location, size, number, and shape, this paper firstly designs a 3D context-guided module based on Kronecker convolution to integrate lesion information from different fields of view, starting from lesion contextual information capture. Then, a 3D spatial attention module is introduced to enhance the representation of lesion features in MRI images. The experiments in this paper confirm that the context-guided module, cross-dimensional cross-attention module, and multidimensional feature similarity module designed for the characteristics of MS lesions are effective, and the proposed attentional context U-Net and multidimensional cross-attention U-Net have greater advantages in the objective evaluation index of lesion segmentation, while being combined with the incremental music teaching approach to assist treatment, which provides a new idea for the intelligent assisted treatment approach. In this paper, from algorithm design to experimental validation, both in terms of accuracy, the operational difficulty of the experiment, consumption of arithmetic power, and time cost, the unique superiority of the artificial intelligence attention-based combined with incremental music teaching adjunctive therapy system proposed in this paper can be seen in the MS lesion segmentation task. [ABSTRACT FROM AUTHOR]

This paper adopts the method of psychological data analysis to conduct in-depth research and analysis on the correlation between teachers' classroom teaching behaviors and students' knowledge acceptance. Firstly, this paper proposes a health factor prediction model, which is specifically divided into clustering and then classification model and a clustering and classification synthesis model. The classroom learning process is coded, sampled, and quantified to obtain data on students' learning behaviors, and a visualization system based on classroom students' learning behaviors is designed and developed to record and analyze students' behaviors in the classroom learning process and grasp students' classroom learning. These two models use algorithms to fine-grained divide the dataset from the perspective of subject users and mental health factors, respectively, and then use decision tree algorithms to classify and predict the mental health factor information by the subject user base information. Second, based on the collected datasets, we designed comparison experiments to validate the clustering-then-classification model and the integrated clustering-classification model and selected the optimal model for comparison. Teachers should increase effective praise and encouragement behaviors; teachers should increase meaningful teacher-student interaction behaviors; teachers should be proficient in teaching media technology to reduce unnecessary time wastage. Strategies to enhance teachers' TPACK include enriching teachers' knowledge base of CK, TK, and PK; developing teachers' integration thinking; and enriching teachers' types of activities for integrating technology. [ABSTRACT FROM AUTHOR]

Simple Summary: Brain MRI scans often require different imaging sequences based on tissue types, posing a common challenge. In our research, we propose a method that utilizes Generative Adversarial Networks (GAN) to translate T2-weighted-Fluid-attenuated-Inversion-Recovery (FLAIR) MRI volumes into T2-Weighted (T2W) volumes, and vice versa. To evaluate the effectiveness of our approach, we introduce a novel evaluation schema that incorporates radiomic features. We train two distinct GAN-based architectures, namely Cycle GAN and Dual Cycle-Consistent Adversarial network (DC2Anet), using 510 pair-slices from 102 patients. Our findings indicate that the generative methods can produce results similar to the original sequence without significant changes in radiometric features. This method has the potential to assist clinicians in making informed decisions based on generated images when alternative sequences are unavailable, or time constraints prevent re-scanning MRI patients. One of the most common challenges in brain MRI scans is to perform different MRI sequences depending on the type and properties of tissues. In this paper, we propose a generative method to translate T2-Weighted (T2W) Magnetic Resonance Imaging (MRI) volume from T2-weight-Fluid-attenuated-Inversion-Recovery (FLAIR) and vice versa using Generative Adversarial Networks (GAN). To evaluate the proposed method, we propose a novel evaluation schema for generative and synthetic approaches based on radiomic features. For the evaluation purpose, we consider 510 pair-slices from 102 patients to train two different GAN-based architectures Cycle GAN and Dual Cycle-Consistent Adversarial network (DC2Anet). The results indicate that generative methods can produce similar results to the original sequence without significant change in the radiometric feature. Therefore, such a method can assist clinics to make decisions based on the generated image when different sequences are not available or there is not enough time to re-perform the MRI scans. [ABSTRACT FROM AUTHOR]

Simple Summary: We propose an automated system that handles numerous problems encountered when diagnosing the presence of numerous types of lesions in the liver based on multiparametric magnetic resonance (MR) images. Using a properly built and processed dataset and deep-learning segmentation algorithms, we devised a method for screening MR images for the presence of focal lesions in the liver. The problems in diagnosing the state of a vital organ such as the liver are complex and remain unresolved. These problems are underscored by frequently published studies on this issue. At the same time, demand for imaging diagnostics, preferably using a method that can detect the disease at the earliest possible stage, is constantly increasing. In this paper, we present liver diseases in the context of diagnosis, diagnostic problems, and possible elimination. We discuss the dataset and methods and present the stages of the pipeline we developed, leading to multiclass segmentation of the liver in multiparametric MR image into lesions and normal tissue. Finally, based on the processing results, each case is classified as either a healthy liver or a liver with lesions. For the training set, the AUC ROC is 0.925 (standard error 0.013 and a p-value less than 0.001), and for the test set, the AUC ROC is 0.852 (standard error 0.039 and a p-value less than 0.001). Further refinements to the proposed pipeline are also discussed. The proposed approach could be used in the detection of focal lesions in the liver and the description of liver tumors. Practical application of the developed multi-class segmentation method represents a key step toward standardizing the medical evaluation of focal lesions in the liver. [ABSTRACT FROM AUTHOR]

This paper incorporates the adaptive neurofuzzy inference system (ANFIS) technique to model the yield of bio-oil. The estimation of this parameter was performed according to pyrolysis conditions and biomass compositions of feedstock. For this purpose, this paper innovates two optimization methods including a genetic algorithm (GA) and particle swarm optimization (PSO). Primary data were gathered from previous studies and included 244 data of biodiesel oils. The findings showed a coefficient determination ( R 2 ) of 0.937 and RMSE of 2.1053 for the GA-ANFIS model, and a coefficient determination ( R 2 ) of 0.968 and RMSE of 1.4443 for PSO-ANFIS. This study indicates the capability of the PSO-ANFIS algorithm in the estimation of the bio-oil yield. According to the performed analysis, this model shows a higher ability than the previously presented models in predicting the target values and can be a suitable alternative to time-consuming and difficult experimental tests. [ABSTRACT FROM AUTHOR]

Simple Summary: Skin cancer is a life-threatening condition. It is difficult to diagnose in its early stages; therefore, we proposed an easy-to-use telemedicine device to tackle skin cancer without expert intervention. The deep learning model automatically detects skin cancer patches on lesions with a credit-card-sized device named Raspberry Pi and a small camera. This paper also presents a digital hair removal algorithm to enhance the quality of medical images for better analysis by medical experts and AI methods. Our method does not need an expert operator; even ordinary people can use it with the instruction manual. It will be useful for developing countries or remote places when there is a scarcity of oncologists. Cancer remains a deadly disease. We developed a lightweight, accurate, general-purpose deep learning algorithm for skin cancer classification. Squeeze-MNet combines a Squeeze algorithm for digital hair removal during preprocessing and a MobileNet deep learning model with predefined weights. The Squeeze algorithm extracts important image features from the image, and the black-hat filter operation removes noise. The MobileNet model (with a dense neural network) was developed using the International Skin Imaging Collaboration (ISIC) dataset to fine-tune the model. The proposed model is lightweight; the prototype was tested on a Raspberry Pi 4 Internet of Things device with a Neo pixel 8-bit LED ring; a medical doctor validated the device. The average precision (AP) for benign and malignant diagnoses was 99.76% and 98.02%, respectively. Using our approach, the required dataset size decreased by 66%. The hair removal algorithm increased the accuracy of skin cancer detection to 99.36% with the ISIC dataset. The area under the receiver operating curve was 98.9%. [ABSTRACT FROM AUTHOR]

This paper presents an automated and noninvasive technique to discriminate COVID-19 patients from pneumonia patients using chest X-ray images and artificial intelligence. The reverse transcription-polymerase chain reaction (RT-PCR) test is commonly administered to detect COVID-19. However, the RT-PCR test necessitates person-to-person contact to administer, requires variable time to produce results, and is expensive. Moreover, this test is still unreachable to the significant global population. The chest X-ray images can play an important role here as the X-ray machines are commonly available at any healthcare facility. However, the chest X-ray images of COVID-19 and viral pneumonia patients are very similar and often lead to misdiagnosis subjectively. This investigation has employed two algorithms to solve this problem objectively. One algorithm uses lower-dimension encoded features extracted from the X-ray images and applies them to the machine learning algorithms for final classification. The other algorithm relies on the inbuilt feature extractor network to extract features from the X-ray images and classifies them with a pretrained deep neural network VGG16. The simulation results show that the proposed two algorithms can extricate COVID-19 patients from pneumonia with the best accuracy of 100% and 98.1%, employing VGG16 and the machine learning algorithm, respectively. The performances of these two algorithms have also been collated with those of other existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper presents a breast cancer detection approach where the convoluted features from a convolutional neural network are utilized to train a machine learning model. Results demonstrate that use of convoluted features yields better results than the original features to classify malignant and benign tumors. Breast cancer is a common cause of female mortality in developing countries. Screening and early diagnosis can play an important role in the prevention and treatment of these cancers. This study proposes an ensemble learning-based voting classifier that combines the logistic regression and stochastic gradient descent classifier with deep convoluted features for the accurate detection of cancerous patients. Deep convoluted features are extracted from the microscopic features and fed to the ensemble voting classifier. This idea provides an optimized framework that accurately classifies malignant and benign tumors with improved accuracy. Results obtained using the voting classifier with convoluted features demonstrate that the highest classification accuracy of 100% is achieved. The proposed approach revealed the accuracy enhancement in comparison with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper aims to develops a new Manta Ray Foraging Optimization Transfer Learning technique that is based on Gastric Cancer Diagnosis and Classification (MRFOTL-GCDC) using endoscopic images. Gastric cancer (GC) diagnoses using endoscopic images have gained significant attention in the healthcare sector. The recent advancements of computer vision (CV) and deep learning (DL) technologies pave the way for the design of automated GC diagnosis models. Therefore, this study develops a new Manta Ray Foraging Optimization Transfer Learning technique that is based on Gastric Cancer Diagnosis and Classification (MRFOTL-GCDC) using endoscopic images. For enhancing the quality of the endoscopic images, the presented MRFOTL-GCDC technique executes the Wiener filter (WF) to perform a noise removal process. In the presented MRFOTL-GCDC technique, MRFO with SqueezeNet model is used to derive the feature vectors. Since the trial-and-error hyperparameter tuning is a tedious process, the MRFO algorithm-based hyperparameter tuning results in enhanced classification results. Finally, the Elman Neural Network (ENN) model is utilized for the GC classification. To depict the enhanced performance of the presented MRFOTL-GCDC technique, a widespread simulation analysis is executed. The comparison study reported the improvement of the MRFOTL-GCDC technique for endoscopic image classification purposes with an improved accuracy of 99.25%. [ABSTRACT FROM AUTHOR]

In this paper, in-depth research and analysis of juvenile delinquency prevention and occupational therapy education guidance using artificial intelligence are conducted, and its response mechanism is designed in this way. Two crime type prediction algorithms based on time-crime type count vectorization and dense neural network and crime type prediction based on the fusion of dense neural network and long- and short-term memory neural network are proposed. The outputs of both are fed into a new neural network for training to achieve the fusion of the two neural networks. Among them, the use of the dense neural network can effectively fit the relationship between the constructed features and crime types. The behavioral manifestations and causes of the formation of deviant behavior in adolescents are discussed. They can only read numerical data, but there is a lot of information in the textual data that is closely related to the training effect. When experimenting, it is necessary to extract knowledge and build applications. The practical work with adolescents with deviant behaviors is again carried out from group work and casework, respectively, with problem diagnosis, needs assessment, and service plan development for specific clients, to carry out relevant practical service work. The causes of juvenile delinquency in the Internet culture are discussed in terms of the Internet environment, juvenile use of the Internet, Internet supervision, and crime prevention education, respectively. The fourth chapter focuses on the analysis of the prevention and control measures for juvenile delinquency in cyberculture. In response to the above-mentioned causes of juvenile delinquency in cyberculture, the prevention and control measures are discussed in four aspects, namely, strengthening the construction of cyberculture and building a healthy cyber environment, strengthening the capacity building of guiding juveniles to use cyber correctly, building a prevention and supervision system to promote the improvement of the legal system, and improving and innovating the crime prevention education in the cyber era. [ABSTRACT FROM AUTHOR]

Helical CT plain scan has high spatial and area resolution, which is beneficial to the extraction of CT features of pulmonary nodules, and is of great significance for the diagnosis and differential diagnosis of pulmonary diseases. In order to deeply study the role of visual sensor image algorithm in CT image, this paper adopts clinical simulation method, data fusion method, and image acquisition method to collect images, analyze CT image features, and simplify the algorithm and create a CT model that can better diagnose secondary tuberculosis and lung cancer. We selected 45 patients with lung disease in this group, with an average age of 38 years. At the same time, the consistency analysis results of the diameter and plain CT value data of the five groups of cases measured by two observers are between 0.82 and 0.88, which has a good consistency. We could find that the nodule diameters of the five groups of cases were different (F =16.99, P < 0.01), and the difference was statistically significant (P < 0.06), indicating that our data are not only accurate but also very reliable. ROC was used to analyze the precise value of CT values in the pulmonary tuberculosis group and lung cancer group, intrapulmonary lymph node group, and pulmonary hamartoma group to determine the cutoff value. The results showed that the AUC values of the pulmonary tuberculosis group and the lung cancer group were 0.788, and the middle was the largest, indicating that the values were guaranteed. The basic realization starts with visual sensor technology and designs a clinical model that can more accurately identify CT images and differential diagnosis. [ABSTRACT FROM AUTHOR]

Objective. Internet of Things (IoT) integrates several technologies where devices learn from the experience of each other thereby reducing human-intervened likely errors. Modern technologies like IoT and machine learning enable the conventional to patient-specific approach transition in healthcare. In conventional approach, the biggest challenge faced by healthcare professionals is to predict a disease by observing the symptoms, monitoring the remote area patient, and also attending to the patient all the time after being hospitalised. IoT provides real-time data, makes decision-making smarter, and provides far superior analytics, and all these to help improve the quality of healthcare. The main objective of the work was to create an IoT-based automated system using machine learning models for symptom-based COVID-19 prognosis. Methods. Comparative analysis of predictive microbiology of COVID-19 from case symptoms using various machine learning classifiers like logistics regression, k-nearest neighbor, support vector machine, random forest, decision trees, Naïve Bayes, and gradient booster is reported here. For the sake of the validation and verification of the models, performance of each model based on the retrieved cloud-stored data was measured for accuracy. Results. From the accuracy plot, it was concluded that k-NN was more accurate (97.97%) followed by decision tree (97.79), support vector machine (97.42), logistics regression (96.50), random forest (90.66), gradient boosting classifier (87.77), and Naïve Bayes (73.50) in COVID-19 prognosis. Conclusion. The paper presents a health monitoring IoT framework having high clinical significance in real-time and remote healthcare monitoring. The findings reported here and the lessons learnt shall enable the healthcare system worldwide to counter not only this ongoing COVID but many other such global pandemics the humanity may suffer from time to come. [ABSTRACT FROM AUTHOR]

The super-resolution (SR) reconstruction of a single image is an important image synthesis task especially for medical applications. This paper is studying the application of image segmentation for lung cancer images. This research work is utilizing the power of deep learning for resolution reconstruction for lung cancer-based images. At present, the neural networks utilized for image segmentation and classification are suffering from the loss of information where information passes through one layer to another deep layer. The commonly used loss functions include content-based reconstruction loss and generative confrontation network. The sparse coding single-image super-resolution reconstruction algorithm can easily lead to the phenomenon of incorrect geometric structure in the reconstructed image. In order to solve the problem of excessive smoothness and blurring of the reconstructed image edges caused by the introduction of this self-similarity constraint, a two-layer reconstruction framework based on a smooth layer and a texture layer is proposed for a medical application of lung cancer. This method uses a global nonzero gradient number constrained reconstruction model to reconstruct the smooth layer. The proposed sparse coding method is used to reconstruct high-resolution texture images. Finally, a global and local optimization models are used to further improve the quality of the reconstructed image. An adaptive multiscale remote sensing image super-division reconstruction network is designed. The selective core network and adaptive gating unit are integrated to extract and fuse features to obtain a preliminary reconstruction. Through the proposed dual-drive module, the feature prior drive loss and task drive loss are transmitted to the super-resolution network. The proposed work not only improves the subjective visual effect but the robustness has also been enhanced with more accurate construction of edges. The statistical evaluators are used to test the viability of the proposed scheme. [ABSTRACT FROM AUTHOR]

The main semantic symbol systems for people to express their emotions include natural language and music. The analysis and establishment of semantic association between language and music is helpful to provide more accurate retrieval and recommendation services for text and music. Existing researches mainly focus on the surface symbolic features and association of natural language and music, which limits the performance and interpretability of applications based on semantic association of natural language and music. Emotion is the main meaning of music expression, and the semantic range of text expression includes emotion. In this paper, the semantic features of music are extracted from audio features, and the semantic matching model of audio emotion analysis is constructed to analyze ethnic music audio emotion through feature extraction ability of deep structure. The model is based on the framework of emotional semantic matching technology and realizes the emotional semantic matching of music fragments and words through semantic emotional recognition algorithm. Multiple experiments show that when W = 0.65 , the recognition rate of multichannel fusion model is 88.42%, and the model can reasonably realize audio emotion analysis. When the spatial dimension of music data changes, the classification accuracy reaches the highest when the spatial dimension is 25. Analysing the semantic association of audio promotes the application of folk music in occupational therapy. [ABSTRACT FROM AUTHOR]

The heart is one of the most important organs of the human body. The role of the heart is to promote blood flow and provide sufficient blood flow to organs and tissues. The research on the heart has important theoretical and clinical significance. Because of the noninvasive and intuitive display of ultrasound image, it can dynamically obtain the heart state and has become the main means to detect the heart dynamics. We analyze the characteristics of cardiac ultrasound image from the medical point of view and signal processing. The heart movement is periodic and rhythmic. The image signal can be decomposed. Firstly, the image is decomposed into high- and low-frequency signals to highlight different dimensional information. Then, the attention model was introduced, focusing on the heart region. Finally, the multidimensional network carrying model was established to achieve cardiac segmentation. The experimental results show that the AOM of the algorithm proposed in this paper reaches 92%, which has a certain degree of advancement and can assist doctors to make accurate diagnosis. [ABSTRACT FROM AUTHOR]

There are many thyroid diseases affecting people all over the world. Many diseases affect the thyroid gland, like hypothyroidism, hyperthyroidism, and thyroid cancer. Thyroid inefficiency can cause severe symptoms in patients. Effective classification and machine learning play a significant role in the timely detection of thyroid diseases. This timely classification will indeed affect the timely treatment of the patients. Automatic and precise thyroid nodule detection in ultrasound pictures is critical for reducing effort and radiologists' mistake rate. Medical images have evolved into one of the most valuable and consistent data sources for machine learning generation. In this paper, various machine learning algorithms like decision tree, random forest algorithm, KNN, and artificial neural networks on the dataset create a comparative analysis to better predict the disease based on parameters established from the dataset. Also, the dataset has been manipulated for accurate prediction for the classification. The classification was performed on both the sampled and unsampled datasets for better comparison of the dataset. After dataset manipulation, we obtained the highest accuracy for the random forest algorithm, equal to 94.8% accuracy and 91% specificity. [ABSTRACT FROM AUTHOR]

To observe the clinical efficacy of early enteral nutrition application in critically ill neurosurgical patients, in this paper, we have developed a prediction model for enteral nutrition support in neurosurgical intensive care patients which is primarily based on an integrated learning algorithm. Additionally, we have compared the prediction performance of each model. The patients were divided into control and combined treatment groups according to the random number table method, and 175 patients in each group were treated with a parenteral method and early enteral nutrition support, respectively. A reentry ICU prediction model based on the integrated learning method random forest, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) was developed, and the prediction performance of integrated learning and logistic regression was compared. The average sensitivity, positive predictive value, negative predictive value, false-positive rate, false-negative rate, area under the receiver operating characteristic curve (AUROC), and Brier score after fivefold cross-validation were used to evaluate model effects, and the best performance model based on the top 10 predictor variables in order of importance was given. Among all models, GBDT (AUROC = 0.858) was better than random forest (AUROC = 0.827) and slightly better than AdaBoost (AUROC = 0.851). The GBDT algorithm gave a higher ranking of importance for variables such as mean arterial pressure, systolic blood pressure, diastolic blood pressure, heart rate, urine volume, and blood creatinine and relatively poorer cardiovascular and renal function in neurosurgical intensive care patients. [ABSTRACT FROM AUTHOR]

This paper constructs an algorithm for youth school violence recognition and an occupational therapy education model for victims through the extraction of action speech features. For the characteristics of violent actions and daily actions, action features in time and frequency domains are extracted and action categories are recognized by BP neural network; for complex actions, it is proposed to decompose complex actions into basic actions to improve the recognition rate; then, LDA dimensionality reduction algorithm is introduced for the problem of the high complexity of algorithm due to high dimensionality of features, and the feature dimensionality is reduced to 8 dimensions by LDA dimensionality reduction algorithm, which reduces the system running time by about 51% and improves the accuracy of violent action recognition by 3.3% while ensuring the overall performance of the system. The LDA dimensionality reduction algorithm reduces the number of features to 8 dimensions, which reduces the running time of the system by 51%, increases the accuracy rate of violent action recognition by 3.3%, and increases the recall rate of violent action recognition by 8.86% while ensuring the overall performance of the system. Based on the classical D-S theory, we proposed an improved D-S evidence fusion algorithm by modifying the original evidence model with a new probability distribution function and constructing new fusion rules, which can solve the fusion conflict problem well. The recall rate for violent actions is increased to 90.0%, thus reducing the missed alarm rate of the system. [ABSTRACT FROM AUTHOR]

Objective. Innovation ability is an important part of children's core literacy and the core goal of science curriculum. As one of the important contents of scientific literacy, innovation ability is the key ability of young children to make informed decisions when facing scientific problems in social and personal life. In order to adapt to the future life, it is very important for children to have the ability to innovate. This research will provide a reference for the cultivation of children's innovative ability. Method. In the process of database design, certain design principles need to be followed. In this paper, the system and user experience are greatly optimized by reasonably constructing table structure, allocating storage space, and establishing indexes. In this system, the MySQL database is used to store system data, such as user registration information, subscription information, and system-provided services, and the data uploaded by users that needs to be processed is stored in Hive. Although the GFP algorithm can solve the problem of load balancing, when the largest conditional pattern base of a frequent item is projected to other nodes, a large amount of data transmission will occur, resulting in increased communication between nodes. In order to solve this problem, the FP-growth parallel algorithm based on traffic optimization gives priority to assigning each frequent item to the node that needs the least traffic when grouping it. Results/Discussion. Experiments show that the TFP algorithm not only satisfies the load balance of nodes but also ensures a small amount of communication between nodes, which is more efficient than the traditional FP-growth parallel algorithm. The survey results of the influencing factors of children's innovation ability match the theoretical hypothesis, and different influencing factors have different effects on each dimension of children's innovation ability. Through the basic fit index of the model, the evaluation of the external quality of the model and the test of the internal quality of the model, it is shown that the survey results of the influencing factors of children's innovation ability match the theoretical hypothesis. The three influencing factors of family participation and investment, teacher teaching, and peer collaboration and communication have a positive role in promoting children's innovation ability. [ABSTRACT FROM AUTHOR]

Malignant melanoma is one of the most threatening cancers to human health. Only 14% of patients with malignant melanoma have a remaining life span of 5 years. At present, there have been some studies looking for potential prognostic indicators of esophageal cancer from the level of genes and infiltrating immune cells, but there are still some problems that need to be resolved urgently. This paper proposes IGHG3 as the immune infiltration of malignant melanoma, which takes into account the computational mathematics. It aims to deduce the characteristics of immune cell infiltration in malignant melanoma and study the relationship between different immune cell infiltration characteristics and prognosis. The method in this article is to establish a computational mathematical model for the immunotherapy of melanoma, then study the method of identification of the affinity of the IGHG3 reagent, and finally obtain the gene expression of immune infiltration. The functions of these methods are, respectively, to predict the dynamic behavior of T cells with two different specificities through mathematical models and to test the matching degree of different concentrations of IGHG3 reagent with the human body. Then use the ssGSEA algorithm to obtain immune infiltration related data and calculate the difference between the weighted empirical cumulative distribution function of all genes in the effect of IGHG3 on melanoma that was carried out. The experimental results showed the computational mathematical method genome and all the remaining genes. In this study, a computational mathematical method to detect the IGHG3 gene expression had a significant inhibitory effect on A375 cells in the experimental group, and the knockdown efficiency reached 85.6%. [ABSTRACT FROM AUTHOR]

This work delivers a novel technique to detect brain tumor with the help of enhanced watershed modeling integrated with a modified ResNet50 architecture. It also involves stochastic approaches to help in developing enhanced watershed modeling. Cancer diseases, primarily the brain tumor, have been exponentially raised which has alarmed researchers from academia and industry. Nowadays, researchers need to attain a more effective, accurate, and trustworthy brain tumor tissue detection and classification approach. Different from traditional machine learning methods that are just targeting to enhance classification efficiency, this work highlights the process to extract several deep features to diagnose brain tumor effectively. This paper explains the modeling of a novel technique by integrating the modified ResNet50 with the Enhanced Watershed Segmentation (EWS) algorithm for brain tumor classification and deep feature extraction. The proposed model uses the ResNet50 model with a modified layer architecture including five convolutional layers and three fully connected layers. The proposed method can retain the optimal computational efficiency with high-dimensional deep features. This work obtains a comprised feature set by retrieving the diverse deep features from the ResNet50 deep learning model and feeds them as input to the classifier. The good performing capability of the proposed model is achieved by using hybrid features of ResNet50. The brain tumor tissue images were extracted by the suggested hybrid deep feature-based modified ResNet50 model and the EWS-based modified ResNet50 model with a high classification accuracy of 92% and 90%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we develop a healthcare biclustering model in the field of healthcare to reduce the inconveniences linked to the data clustering on gene expression. The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and remove the duplication of broad gene information components. Moreover, because of its adequacy in the problem where populations of potential solutions allow exploration of a greater portion of the research area, machine learning or heuristic algorithm has become extensively used for healthcare biclustering in the field of healthcare. The study is evaluated in terms of average match score for nonoverlapping modules, overlapping modules through the influence of noise for constant bicluster and additive bicluster, and the run time. The results show that proposed FCM blustering method has higher average match score, and reduced run time proposed FCM than the existing PSO-SA and fuzzy logic healthcare biclustering methods. [ABSTRACT FROM AUTHOR]

Simple Summary: Acute myeloid leukemia (AML) is the second most frequent leukemia entity in children and adolescents, and definitely the most aggressive variant. Multiparameter flow-cytometry is one of the methodologies most useful to monitor the number of remaining leukemic cells in bone marrow (minimal residual disease, MRD) in AML patients, because it is widely available and applicable to most patients. However, AML flow cytometry data show very complex patterns and identifying leukemic cells in the data is subjective, time-consuming and requires experienced operators who are not available world-wide. In this paper, we approach automatic assessment of AML flow cytometry samples with a novel semi-supervised machine learning model, leveraging implicit expert knowledge stored in a collection of manually assessed samples. Because AML data exhibit a high degree of variability in the patterns of blast cell populations that is difficult to model, the model detects anomalies starting from the appearance of normal cell populations. Leukemia is the most frequent malignancy in children and adolescents, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) as the most common subtypes. Minimal residual disease (MRD) measured by flow cytometry (FCM) has proven to be a strong prognostic factor in ALL as well as in AML. Machine learning techniques have been emerging in the field of automated MRD quantification with the objective of superseding subjective and time-consuming manual analysis of FCM-MRD data. In contrast to ALL, where supervised multi-class classification methods have been successfully deployed for MRD detection, AML poses new challenges: AML is rarer (with fewer available training data) than ALL and much more heterogeneous in its immunophenotypic appearance, where one-class classification (anomaly detection) methods seem more suitable. In this work, a new semi-supervised approach based on the UMAP algorithm for MRD detection utilizing only labels of blast free FCM samples is presented. The method is tested on a newly gathered set of AML FCM samples and results are compared to state-of-the-art methods. We reach a median F 1 -score of 0.794, while providing a transparent classification pipeline with explainable results that facilitates inter-disciplinary work between medical and technical experts. This work shows that despite several issues yet to overcome, the merits of automated MRD quantification can be fully exploited also in AML. [ABSTRACT FROM AUTHOR]