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Introduction: While meaningless gross motor imitation (GMI) is a common challenge for children diagnosed with autism spectrum disorder (ASD), this topic has not attracted much attention and few appropriate test paradigms have been developed. Methods: The current study proposed a wrist rotation imitation (WRI) task (a meaningless GMI assignment), and established a WRI ability evaluation system using low-cost wearable inertial sensors, which acquired the simultaneous data of acceleration and angular acceleration during the WRI task. Three metrics (i.e., total rotation time, rotation amplitude, and symmetry) were extracted from those data of acceleration and angular acceleration, and then were adopted to construct classifiers based on five machine learning (ML) algorithms, including k-nearest neighbors, linear discriminant analysis, naive Bayes, support vector machines, and random forests. To illustrate our technique, this study recruited 49 ASD children (aged 3.5-6.5 years) and 59 age-matched typically developing (TD) children. Results: Findings showed that compared with TD children, those with ASD may exhibit shorter total rotation time, lower rotation amplitude, and weaker symmetry. This implies that children with ASD might exhibit decreased WRI abilities. The classifier with the naive Bayes algorithm outperformed than other four algorithms, and achieved a maximal classification accuracy of 88% and a maximal AUC value of 0.91. Two metrics (i.e., rotation amplitude and symmetry) had high correlations with the gross and fine motor skills [evaluated by Gesell Developmental Schedules-Third Edition and Psychoeducational Profile-3 (PEP-3)]. While, the three metrics had no significant correlation with the visual-motor imitation abilities (evaluated by the subdomain of PEP-3) and the ASD symptom severity [evaluated by the Childhood Autism Rating Scale (CARS)] . Discussion: The strengths of this study are associated with the low-cost measurement system, correlation between the WRI metrics and clinical measures, decreased WRI abilities in ASD, and high classification accuracy. [ABSTRACT FROM AUTHOR]

In the modern era of the digital world, digital devices have taken over many aspects of our lives, and the healthcare industry is no exception. Clinicians are also increasingly migrating patient healthcare data from paper to electronic formats. With the advancement of recent technology, the automated Clinical Decision Support System (CDSS) aims to make better prognoses and improve the quality of healthcare delivery. The proposed system focuses on significant improvement to satisfy the expectation of patients and doctors. In this work, we propose a framework to classify different diseases and provide outcomes to the patient in the form of recommendations. We focused on preprocessing and feature selection techniques to improve the performance and quality of care. Firstly, data is collected and preprocessed using encoding categorical features, min–max scaling and removing null and duplicate entries. After that, a Rough Set Theory (RST) is applied to select highly relevant and nonredundant features to reduce the dimension of the datasets. Then, six popular machine learning classifiers viz., K Nearest Neighbors (KNN), Linear Support Vector Machine (LSVM), Radial Basis Function Support Vector Machine (RBF SVM), Decision Tree (DT), Random Forest (RF) and Naive Bayes (NB) were employed on four different medical datasets such as breast cancer, heart failure, post-operative patient data and thyroid collected from UCI repository. Performance was compared with proposed classifiers and existing state-of-the-art classifiers in terms of accuracy, precision, recall and f1-score. Our proposed classifier DT achieved better accuracy for breast cancer, post-operative patient and thyroid while KNN achieved better accuracy for heart failure. Hence, overall performance strongly suggests that the proposed framework may help medical experts to diagnose various diseases more accurately. [ABSTRACT FROM AUTHOR]

In this paper, the implementation of a new pupil detection system based on artificial intelligence techniques suitable for real-time and real-word applications is presented. The proposed AI-based pupil detection system uses a classifier implemented with slim-type neural networks, with its classes being defined according to the possible positions of the pupil within the eye image. In order to reduce the complexity of the neural network, a new parallel architecture is used in which two independent classifiers deliver the pupil center coordinates. The training, testing, and validation of the proposed system were performed using almost 40,000 eye images with a resolution of 320 × 240 pixels and coming from 20 different databases, with a high degree of generality. The experimental results show a detection rate of 96.29% at five pixels with a standard deviation of 3.38 pixels for all eye images from all databases and a processing speed of 100 frames/s. These results indicate both high accuracy and high processing speed, and they allow us to use the proposed solution for different real-time applications in variable and non-uniform lighting conditions, in fields such as assistive technology to communicate with neuromotor-disabled patients by using eye typing, in computer gaming, and in the automotive industry for increasing traffic safety by monitoring the driver's cognitive state. [ABSTRACT FROM AUTHOR]

Parkinson disease (PD) is a type of neurodegenerative disorder that affects the motor movement of the patient. But each technique has its own advantages or disadvantages. In gene, speech and handwriting data model, the feature extraction and reduction is an important step for efficient classification. These two steps require proper attention for selection and also require high processing time as compared to other data model like images. Because in image modality, the deep learning algorithm can be applied that can perform all process and automate the classification. As compared to these domains, the signal produces better and best results. Because the electroencephalogram (EEG) signal are taken from the brain using electrodes and it helps to observe the brain signals effectively and immediately as compared to the other data modals. Hence, in this paper, the wavelet transform will be used to decompose the signals and statistical features will be extracted from the transformed signal. Here, the satin bower bird optimization will be used for both type of wavelet selection and feature reduction process for final classification. The reduced feature set will be classified using Ensemble Neural Network type including InceptionV3, DenseNet, MobileNet, Xception, and NasNet) recently proposed for medical image classification. The whole process will be realized using MATLAB R2021a software and its performance will be evaluated in terms of Accuracy and is compared against Automated Tunable Q-wavelet transform performance. The proposed ensemble method, employing EEG signal processing and neural networks, achieved a 97% success rate in discriminating PD datasets, surpassing Convolutional Neural Network (CNN) and Machine Learning (ML) classifications (88% –92%). Utilizing MATLAB R2021a, its superiority over Q-wavelet transform was evident, signifying improved PD dataset discrimination. [ABSTRACT FROM AUTHOR]

Diagnosing plant disease is a major role to reduce adequate losses in yield production, which further leads to economic losses. The various disease control measures are accessible without a proper diagnosis of the disease which results in a waste of expenses and time. The diagnosis of disease using images leads to unsatisfactory results in the prevalent methods due to the image clarity. It is mainly caused by the worst performance of the existing pre-trained image classifiers. This issue can be controlled by the SMoGW-deep convolutional neural network (deep CNN) classifier for the accurate and precise classification of plant leaf disease. The developed method transforms the poor-quality captured images into high quality by the preprocessing technique. The preprocessed input images contain pixels on their dimension and also the value of the threshold is analyzed by the Otsu method by which the particular disease-affected region is extracted based on the image pixels. The region of interest is separated from the other parts of the input leaf image using the K-means segmentation technique. The stored features in the feature vector are fed forward to the deep CNN classifier for training and are optimized by the SMoGW optimization approach. The experiments are done and achieved an accuracy of 94.5% sensitivity of 94.525%, specificity of 94.6%, precision of 95% with 90% of training data and under K-fold training with 95% of accuracy, 95% of sensitivity, 94.1% of specificity, and 92.1% of precession is achieved for the SMoGW-optimized classifier approach that is superior to the prevalent techniques for disease classification and detection. The potential, as well as the capability of the proposed method, is experimentally demonstrated for plant leaf disease classification and identification. [ABSTRACT FROM AUTHOR]

We present an efficient construction method for object recognition based on speckle learning using the optical memory effect. An object classifier based on speckle learning without the process of reducing or eliminating scattering and with a simple optical setup has been previously reported, but it requires a large number of training images to improve the performance of the classifier. This method is not applicable for bioimaging because of the difficulty of collecting training images caused by position control and phototoxicity of target cells. In our method, a wide variety of training images are augmented by a computer from a few speckle intensity images in the working range of the optical memory effect. We experimentally demonstrated our method with a 4f-optical system implementing the optical memory effect. As a result, the constructed binary classifier showed high accuracy under various scattering conditions and resolutions of the test image. [ABSTRACT FROM AUTHOR]

In the rapidly evolving domain of Industry 4.0, effective management of software updates is crucial for maintaining system continuity and security. This paper presents a novel machine learning-based approach for a prompt and effective triage of software updates, leveraging an evaluation of six release note classifiers to categorize updates by component type, release type, and security risk. Our methodology, tested on a dataset of 1,000 release notes commonly encountered in Industry 4.0 ecosystems, demonstrates Logistic Regression as the most accurate classifier. The findings not only highlight the practical applicability of our approach in real-world data but also set the foundation for future enhancements to streamline the machine learning triage process further. [ABSTRACT FROM AUTHOR]

The ability to accurately anticipate heart failure risks in a timely manner is essential because heart failure has been identified as one of the leading causes of death. In this paper, we propose a novel method for identifying cardiovascular heart disease by utilizing a K-means clustering and Random Forest classifier combination. Based on their clinical and demographic traits, patients were classified into either healthy or diseased groups using the Random Forest classifier after being clustered using the K-means method. The performance of the proposed hybrid approach was evaluated using a dataset of patient records and compared with traditional diagnostic methods, namely support vector machine (SVM), logistic regression, and Naive Bayes classifiers. The outcomes indicated that the proposed hybrid method attained a high accuracy in diagnosing heart disease, with an overall accuracy of 96.8%. Additionally, the method showed a good performance in classifying patients at high risk of heart disease: the sensitivity reached 96.3% and the specificity reached 97.2%. In conclusion, the proposed method of combining K-means clustering and a Random Forest classifier is a promising approach for the accurate and efficient identification of heart disease. Further studies are needed to validate the proposed method in larger and more diverse patient populations. [ABSTRACT FROM AUTHOR]

Physical layer (PHY) authentication methods provide spatial security by exploiting the unique channel between two users. In recent years, many studies focused on substituting traditional threshold-based detection mechanisms with machine/deep learning classifiers to solve the threshold selection problem and obtain better detection accuracy. However, these studies assume that receivers have access to spoofer's channel information at the training of the classifier, which is unrealistic for real-time scenarios. In this study, we propose a PHY-authentication architecture for wireless multiple access channels (W-MACs) that removes this assumption and works without any prior information about the spoofer. The proposed method is designed for multi-user systems and is suitable for any classifier model or communication protocol. The feasibility and the performance of the proposed method are investigated via computer simulations and compared with a benchmark model. The results proved the feasibility of the proposed method as it can detect spoofers successfully without requiring spoofers' channel information. [ABSTRACT FROM AUTHOR]

Recently, there have been notable advancements in video editing software. These advancements have allowed novices or those without access to advanced computer technology to generate videos that are visually indistinguishable to the human eye from real ones to the human observer. Therefore, the application of deepfake technology has the potential to expand the scope of identity theft, which poses a significant risk and a formidable challenge to global security. The development of an effective approach for detecting fake videos is necessary. Here, we introduce a novel methodology that employs a convolutional neural network (CNN) and Gaussian mixture model (GMM) to effectively differentiate between fake and real images or videos. The proposed methodology presents a novel CNN-GMM architecture in which the fully connected (FC) layer in the CNN is replaced with a customized Gaussian mixture model (GMM) fully connected layer. The GMM layer utilizes a weighted set of Gaussian probability density functions (PDFs) to represent the distribution of data frequencies in both real and fake images. This representation indicates there is a shift in the distribution of the manipulated images due to added noise. The CNN-GMM model demonstrates the ability to accurately identify variations resulting from different types of deepfakes within the probability distribution. It achieves a high level of classification accuracy, reaching up to 100% in training accuracy and up to 96% in validation accuracy. Notwithstanding the ratio of the genuine class to the counterfeit class being 16.6% to 83.4%, the CNN-GMM model exhibited high-performance metrics in terms of recall, accuracy, and F-score when classifying the least genuine class. [ABSTRACT FROM AUTHOR]

The current existing classifiers for distinguishing malignant from benign pulmonary nodules is limited by effectiveness or clinical practicality. In our study, we aimed to develop and validate a gene classifier for lung cancer diagnosis. To identify the genes involved in this process, we used the weighted gene co-expression network analysis to analyze gene expression datasets from Gene Expression Omnibus (GEO). We identified the three most relevant modules associated with malignant nodules and performed functional enrichment analysis on them. The results indicated significant involvement in metabolic, immune-related, cell cycle, and viral-related processes. All three modules showed enrichment in metabolic reprogramming pathways. Based on these genes, we intersected genes from the three modules with metabolic reprogramming-related genes and further intersected with differentially expressed genes to get 78 genes. After machine learning algorithms and manual selection, we finally got a nine-gene classifier consisting of SEC24D, RPSA, PSME3, PSMD8, PSMB7, NCOA1, MED12, LPCAT1, and AKR1C3. Our developed and validated classifier-based model demonstrated good discrimination, with an area under the curve (AUC) of 0.763 in the development cohort, 0.744 in the internal validation cohort, and 0.718 in the external validation cohort, and outperformed previous clinical models. Moreover, the addition of nodule size improved the predictive capability of the classifier. We further verify the expression of the gene in the classifier using TCGA lung cancer samples and found eight of the genes showed significant differential expression in lung adenocarcinoma while all nine genes showed significant differential expression in lung squamous carcinoma. Our findings underscore the significance of metabolic reprogramming pathways in patients with malignant pulmonary nodules, and our gene classifier can assist clinicians in differentiating benign from malignant pulmonary nodules in clinical settings.

Background: For pediatric patients with solid abdominal tumors, early diagnosis can guide clinical treatment decisions, and comprehensive preoperative evaluation is essential to reduce surgical risk. The aim of this study was to explore the feasibility of multiphase enhanced CT-based transformer in the early diagnosis of tumors and prediction of surgical risk events (SRE). Methods: A total of 496 pediatric patients with solid abdominal tumors were enrolled in the study. With Swin transformer, we constructed and trained two Swin-T models based on preoperative multiphase enhanced CT for personalized prediction of tumor type and SRE status. Subsequently, we comprehensively evaluated the performance of each model and constructed four benchmark models for performance comparison. Results: There was no significant difference in SRE status between tumor types. In the diagnostic task, areas under the receiver operating characteristic curves (AUC) of the Swin-T model were 0.987 (95 % CI, 0.973–0.997) and 0.844 (95 % CI, 0.730–0.940) in the training and validation cohorts, respectively. In predicting SRE, AUCs of the Swin-T model were 0.920 (95 % CI, 0.885–0.948) and 0.741 (95 % CI, 0.632–0.838) in the training and test cohorts, respectively. The Swin-T model achieved the best performance in both classification tasks compared to benchmark models. Conclusion: The Swin-T model is a promising tool to assist pediatricians in the differential diagnosis of abdominal tumors and in comprehensive preoperative evaluation.

Abnormal activity in the cerebellar nuclei can be used to predict motor symptoms and induce them experimentally, pointing to potential therapeutic strategies.

The continued emergence of SARS-CoV-2 variants is one of several factors that may cause false-negative viral PCR test results. Such tests are also susceptible to false-positive results due to trace contamination from high viral titer samples. Host immune response markers provide an orthogonal indication of infection that can mitigate these concerns when combined with direct viral detection. Here, we leverage nasopharyngeal swab RNA-seq data from patients with COVID-19, other viral acute respiratory illnesses, and nonviral conditions (n = 318) to develop support vector machine classifiers that rely on a parsimonious 2-gene host signature to diagnose COVID-19. We find that optimal classifiers include an interferon-stimulated gene that is strongly induced in COVID-19 compared with nonviral conditions, such as IFI6, and a second immune-response gene that is more strongly induced in other viral infections, such as GBP5. The IFI6+GBP5 classifier achieves an area under the receiver operating characteristic curve (AUC) greater than 0.9 when evaluated on an independent RNA-seq cohort (n = 553). We further provide proof-of-concept demonstration that the classifier can be implemented in a clinically relevant RT-qPCR assay. Finally, we show that its performance is robust across common SARS-CoV-2 variants and is unaffected by cross-contamination, demonstrating its utility for improved accuracy of COVID-19 diagnostics. IMPORTANCE In this work, we study upper respiratory tract gene expression to develop and validate a 2-gene host-based COVID-19 diagnostic classifier and then demonstrate its implementation in a clinically practical qPCR assay. We find that the host classifier has utility for mitigating false-negative results, for example due to SARS-CoV-2 variants harboring mutations at primer target sites, and for mitigating false-positive viral PCR results due to laboratory cross-contamination. Both types of error carry serious consequences of either unrecognized viral transmission or unnecessary isolation and contact tracing. This work is directly relevant to the ongoing COVID-19 pandemic given the continued emergence of viral variants and the continued challenges of false-positive PCR assays. It also suggests the feasibility of pan-respiratory virus host-based diagnostics that would have value in congregate settings, such as hospitals and nursing homes, where unrecognized respiratory viral transmission is of particular concern.

Abstract Background The unknown tissue of origin in head and neck cancer of unknown primary (hnCUP) leads to invasive diagnostic procedures and unspecific and potentially inefficient treatment options for patients. The most common histologic subtype, squamous cell carcinoma, can stem from various tumor primary sites, including the oral cavity, oropharynx, larynx, head and neck skin, lungs, and esophagus. DNA methylation profiles are highly tissue-specific and have been successfully used to classify tissue origin. We therefore developed a support vector machine (SVM) classifier trained with publicly available DNA methylation profiles of commonly cervically metastasizing squamous cell carcinomas (n = 1103) in order to identify the primary tissue of origin of our own cohort of squamous cell hnCUP patient’s samples (n = 28). Methylation analysis was performed with Infinium MethylationEPIC v1.0 BeadChip by Illumina. Results The SVM algorithm achieved the highest overall accuracy of tested classifiers, with 87%. Squamous cell hnCUP samples on DNA methylation level resembled squamous cell carcinomas commonly metastasizing into cervical lymph nodes. The most frequently predicted cancer localization was the oral cavity in 11 cases (39%), followed by the oropharynx and larynx (both 7, 25%), skin (2, 7%), and esophagus (1, 4%). These frequencies concord with the expected distribution of lymph node metastases in epidemiological studies. Conclusions On DNA methylation level, hnCUP is comparable to primary tumor tissue cancer types that commonly metastasize to cervical lymph nodes. Our SVM-based classifier can accurately predict these cancers’ tissues of origin and could significantly reduce the invasiveness of hnCUP diagnostics and enable a more precise therapy after clinical validation.

The purpose of this work is to modify the learning mechanism of a collective classifier in order to provide learning by population dynamics alone, without requiring an external sorting device. A collective classifier is an ensemble of non-identical simple elements, which do not have any intrinsic dynamics neither variable parameters; the classifier admits learning by adjusting the composition of the ensemble, which was provided in the preceding literature by selecting the ensemble elements using a sorting device. Methods. The population dynamics model of a collective classifier is extended by adding a “learning subsystem”, which is controlled by a sequence of training examples and, in turn, controls the strength of intraspecific competition in the population dynamics. The learning subsystem dynamics is reduced to a linear mapping with random parameters expressed via training examples. The solution to the mapping is an asymptotically stationary Markovian random process, for which we analytically find asymptotic expectation and show its variance to vanish in the limit under the specified assumptions, thus allowing an approximate deterministic description of the coupled population dynamics based on available results from the preceding literature. Results. We show analytically and illustrate it by numerical simulation that the decision rule of our classifier in the course of learning converges to the Bayesian rule under assumptions which are essentially in line with available literature on collective classifiers. The implementation of the required competitive dynamics does not require an external sorting device. Conclusion. We propose a conceptual model for a collective classifier, whose learning is fully provided by its own population dynamics. We expect that our classifier, similarly to the approaches taken in the preceding literature, can be implemented as an ensemble of living cells equipped with synthetic genetic circuits, when a mechanism of population dynamics with synthetically controlled intraspecific competition becomes available.

Sepsis is a potentially fatal condition characterized by the failure of one or more organs due to a disordered host response to infection. The development of sepsis is closely linked to immune dysfunction. As a result, immunotherapy has gained traction as a promising approach to sepsis treatment, as it holds the potential to reverse immunosuppression and restore immune balance, thereby improving the prognosis of septic patients. However, due to the highly heterogeneous nature of sepsis, it is crucial to carefully select the appropriate patient population for immunotherapy. This review summarizes the current and evolved treatments for sepsis-induced immunosuppression to enhance clinicians' understanding and practical application of immunotherapy in the management of sepsis.

The ability to accurately anticipate heart failure risks in a timely manner is essential because heart failure has been identified as one of the leading causes of death. In this paper, we propose a novel method for identifying cardiovascular heart disease by utilizing a K-means clustering and Random Forest classifier combination. Based on their clinical and demographic traits, patients were classified into either healthy or diseased groups using the Random Forest classifier after being clustered using the K-means method. The performance of the proposed hybrid approach was evaluated using a dataset of patient records and compared with traditional diagnostic methods, namely support vector machine (SVM), logistic regression, and Naive Bayes classifiers. The outcomes indicated that the proposed hybrid method attained a high accuracy in diagnosing heart disease, with an overall accuracy of 96.8%. Additionally, the method showed a good performance in classifying patients at high risk of heart disease: the sensitivity reached 96.3% and the specificity reached 97.2%. In conclusion, the proposed method of combining K-means clustering and a Random Forest classifier is a promising approach for the accurate and efficient identification of heart disease. Further studies are needed to validate the proposed method in larger and more diverse patient populations.

Automatic Modulation Classification (AMC) is an essential technology that is widely applied into various communications scenarios. In recent years, many Machine Learning and Deep-Learning methods have been introduced into AMC, and a lot of them apply different approaches to eliminate interference in complex Multiple-Input and Multiple-Output (MIMO) signals and improve classification performance. However, in practical communication systems, the perfect elimination of MIMO signal interference is impossible, and therefore classification performance suffers. In this paper, we propose a new AMC algorithm for MIMO system based on mutual information (MI) features extraction, which does not require a large amount of training data and the elimination of MIMO signal interference. In this approach, features based on mutual information are extracted using In-Phase and Quadrature (IQ) constellation diagrams of MIMO signals, which have not been explored previously. Our method can be effective since mutual information considers the interdependencies among variables and measures how much information about one variable reduces uncertainty about another, providing a valuable perspective for extracting higher-level and interesting features from the data. The effectiveness of our method is evaluated on several model and real-world datasets, and its applicability is proven.

The spectral images provide the dynamic characteristics of the voice signal in the time and frequency domains. However, extracting the predominant spectral features from the voice samples is still challenging. This work generates cochleagram images to unveil detailed spectral content of the voice samples to recognize dysphonic voice. Both sustained vowel (‘/a/’) and sentence voice samples are considered to include phonation, respiration, and resonance of the vocal tone. Also, gender bias is eliminated by considering male and female voice samples separately, as they have structurally different vocal tracts, pharynx, and oral cavities. The simulation results show that the cochleagram, coined with a designed pre-trained convolutional neural network (CNN), can achieve 95% accuracy in identifying dysphonic voices with sentence samples. A robust, noninvasive, and automated voice pathology detection system is effectively generated through perceptual analysis of voice signals. The proposed automated pathological voice detection system can objectively correlate the clinical findings and assist in monitoring the treatment progress of dysphonic voice on top of subjective assessment by clinicians.

Malware targeting user privacy has seen a surge in recent times, attributed to evolving global regulations and the boost of electronic commerce and online services. Moreover, recognizing privacy malware that employs obfuscation as evasion mechanism presents a major challenge due to its dynamics, resilience, and polymorphism at runtime, necessitating the application of forensic techniques such as memory dumping analysis in order to reveal suitable patterns and behaviors that enable its subsequent detection and classification. In this paper, we present three obfuscated privacy malware classifiers trained on the CIC-MalMem-2022 dataset. These solutions include a binary classifier to distinguish benign from malicious samples using logistic regression (LR), a multiclass classifier that further categorizes benign, spyware, ransomware, and trojan horse obfuscated privacy malware; and a more detailed multiclass classifier capable of discriminating benign samples from fifteen specific obfuscated privacy malware families. Multiclass classifiers were built using several traditional machine learning algorithms and a novel Deep Neural Network (DNN). We applied the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalances. In particular, our results demonstrate that DNN outperforms traditional machine learning algorithms, yielding statistically significant improvements in key metrics. These achievements reach practical thresholds, suggesting the potential for enhanced malware protection systems. The dataset and all the coding files required for experiments reproducibility are publicly available at https://github.com/dcevallossalas/PrivacyMalwareClassifiers.

Background: The unknown tissue of origin in head and neck cancer of unknown primary (hnCUP) leads to invasive diagnostic procedures and unspecific and potentially inefficient treatment options for patients. The most common histologic subtype, squamous cell carcinoma, can stem from various tumor primary sites, including the oral cavity, oropharynx, larynx, head and neck skin, lungs, and esophagus. DNA methylation profiles are highly tissue-specific and have been successfully used to classify tissue origin. We therefore developed a support vector machine (SVM) classifier trained with publicly available DNA methylation profiles of commonly cervically metastasizing squamous cell carcinomas (n = 1103) in order to identify the primary tissue of origin of our own cohort of squamous cell hnCUP patient's samples (n = 28). Methylation analysis was performed with Infinium MethylationEPIC v1.0 BeadChip by Illumina. Results: The SVM algorithm achieved the highest overall accuracy of tested classifiers, with 87%. Squamous cell hnCUP samples on DNA methylation level resembled squamous cell carcinomas commonly metastasizing into cervical lymph nodes. The most frequently predicted cancer localization was the oral cavity in 11 cases (39%), followed by the oropharynx and larynx (both 7, 25%), skin (2, 7%), and esophagus (1, 4%). These frequencies concord with the expected distribution of lymph node metastases in epidemiological studies. Conclusions: On DNA methylation level, hnCUP is comparable to primary tumor tissue cancer types that commonly metastasize to cervical lymph nodes. Our SVM-based classifier can accurately predict these cancers' tissues of origin and could significantly reduce the invasiveness of hnCUP diagnostics and enable a more precise therapy after clinical validation. [ABSTRACT FROM AUTHOR]

The Associative Pattern Classifier (APC) was designed as an associative memory, focusing particularly on pattern classification. This implies that the training memory is constructed in a single operation and pattern classification also occurs in a single process. It is important to note that the APC translates the input patterns through a translation vector, which represents the average of all input patterns. Until now, there is no theoretical framework to explain the inner workings of the APC. Its relevance is inferred from the fact that several studies have been conducted using it as a foundation. This paper seeks to provide a theoretical comprehension of the APC’s operation to facilitate future enhancements. We found the APC creates a system in static equilibrium through concurrent vectors at the origin (translation vector), resulting in a balanced separation of patterns. However, the APC cannot achieve complete pattern separation because of the presence of a neutral region. The neutral region is defined by all the points that define the separation hyperplanes. The points over the hyperplanes cannot be classified by the APC. Additionally, we discovered that the APC is unable to accurately classify the translation vector, which could be included as part of the input patterns. Our previous research showed that the APC is unsuccessful in achieving the linear separation of the AND function. In this research, we also broaden the examination of the AND function to illustrate that achieving linear separation is not feasible because the separation line represents a neutral region. The APC demonstrated exceptional performance when tested with artificial datasets where patterns were distributed over balanced regions, thus operating as an efficient multiclass and non-linear classifier. Nevertheless, the performance of the APC is lower when tested with real-world databases, making the APC inaccurate due to its restricted inner workings. [ABSTRACT FROM AUTHOR]

Background of the study: AML classification tools have been developed to stratify the risk at AML diagnosis. There is a need to evaluate these tools in the current therapeutic era. Cohort characteristics: In this retrospective study, we compared five classifiers: ELN 2017, ELN 2022, ALFA classifier, Papaemmanuil et al. classifier, and Lindsley et al. classifier, in a real‐life cohort of 281 patients newly diagnosed for AML in Nice University Hospital. In our cohort median age was 68 years old, sex ratio was M/F 56%/44%, performance status was lower than 2 in 73.1% of patients, AML subtype was "De novo" in 71.5%, "secondary" in 22.4%, and "therapy‐related" in 6.0% of patients. Intensive chemotherapy was used in 53.0% of patients, and non‐intensive chemotherapy in 40.6% of patients. Molecular analysis was available in a large majority of patients and the main mutations found were NPM1 (22.7%), DNMT3A (17.4%), TP53 (13.1%), TET2 (12.4%), and FLT3‐ITD (12.4%). Results: In our findings, the comparison of overall survival between the three prognostic groups in the global cohort was statistically significant in all classifiers: ELN 2017 p < 0.0001, ELN 2022 p < 0.0001, ALFA classifier p < 0.0001, Papaemmanuil classifier p < 0.0001, Lindsley classifier p = 0.001. ELN 2017, ELN 2022, ALFA classifier, Papaemmanuil classifier, and Lindsley classifier were calculated respectively in 99%, 99%, 89%, 90%, and 89% of patients. Conclusions: Using Akaike's information criteria (AIC) to compare all five classifiers, ELN 2022 is the best classifier into younger and older patients and for prognosis. [ABSTRACT FROM AUTHOR]

Sepsis is a potentially fatal condition characterized by the failure of one or more organs due to a disordered host response to infection. The development of sepsis is closely linked to immune dysfunction. As a result, immunotherapy has gained traction as a promising approach to sepsis treatment, as it holds the potential to reverse immunosuppression and restore immune balance, thereby improving the prognosis of septic patients. However, due to the highly heterogeneous nature of sepsis, it is crucial to carefully select the appropriate patient population for immunotherapy. This review summarizes the current and evolved treatments for sepsis-induced immunosuppression to enhance clinicians' understanding and practical application of immunotherapy in the management of sepsis. [ABSTRACT FROM AUTHOR]

Congenital heart defect (CHD) is one of the most serious congenital deformities in a fetus. About 31% to 55% of CHDs are the primary cause that leads to life-threatening problem among neonates, hence sonographers emphasize the importance of prenatal CHD screening. Among 18 types of CHDs, the asymmetric appearance of the heart seems to be a challenging part. Hypoplastic left heart syndrome (HLHS) is a critical and rare CHD, with an underdeveloped left heart chamber of the fetus. This prenatal CHD can be diagnosed between 17 to 21 weeks of gestation period. Though ultrasound provides a good diagnostic result, prenatal diagnosis is still a challenging area due to its speckle noise and irregular appearance of the heart chambers. In this context, the basic step is to appropriately select the pre-processing algorithm, one such algorithm is the Fuzzy based maximum likelihood estimation technique (FMLET). Right ventricle left ventricle ratio (RVLVR) and cardiac thoracic ratio (CTR) are the two important features required for manual diagnosis of the ultrasound images. Hence, morphological operations such as open, close, thinning and thickening helps to extract the diagnostically important features inherent in the images. Finally, the computer aided decision support (CADS) system is designed with pre-processing module, morphological module and adaptive neuro fuzzy (ANFC) classifier module. ANFC is investigated as the good classifiers to help the experts in terms of self-learning with higher diagnostic rate. The proposed CADS proven with 91% of diagnostic accuracy and the standardized area under the ROC curve obtained was 0.9137. [ABSTRACT FROM AUTHOR]

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The spatial distribution of tumor infiltrating lymphocytes (TILs) defines several histologically and clinically distinct immune subtypes—desert (no TILs), excluded (TILs in stroma), and inflamed (TILs in tumor parenchyma). To date, robust classification of immune subtypes still requires deeper experimental evidence across various cancer types. Here, we aimed to investigate, define, and validate the immune subtypes in melanoma by coupling transcriptional and histological assessments of the lymphocyte distribution in tumor parenchyma and stroma. We used the transcriptomic data from The Cancer Genome Atlas melanoma dataset to screen for the desert, excluded, and inflamed immune subtypes. We defined subtype-specific genes and used them to construct a subtype assignment algorithm. We validated the two-step algorithm in the qPCR data of real-world melanoma tumors with histologically defined immune subtypes. The accuracy of a classifier encompassing expression data of seven genes (immune response-related: CD2, CD53, IRF1, and CD8B; and stroma-related: COL5A2, TNFAIP6, and INHBA) in a validation cohort reached 79%. Our findings suggest that melanoma tumors can be classified into transcriptionally and histologically distinct desert, excluded, and inflamed subtypes. Gene expression-based algorithms can assist physicians and pathologists as biomarkers in the rapid assessment of a tumor immune microenvironment while serving as a tool for clinical decision making. [ABSTRACT FROM AUTHOR]

In industrial applications, rotating machines operate under real-time variable speed and load regimes. In the presence of faults, the degradation of critical components is accelerated significantly. Therefore, robust monitoring algorithms able to identify these faults become crucial. In the literature, it is hard to find comprehensive monitoring systems that include variable speed and load regimes with combined gearbox faults using electrical and vibration signals. For this purpose, a novel signal processing methodology including a geometric classification technique is proposed. This methodology is based on using different types of sensors such as current, voltage and vibration sensors with a regime normalization, which allows the grouping of different regimes belonging to the same health state. It consists of reducing dispersion between the class observations and separating other classes representing different health states including the variation in speed and load. Then, a peripheral threshold is proposed in our classifier to diagnose new health states. To verify the effectiveness of the methodology, current, voltage and vibration data from a gearbox system are collected under variable speed and load levels. [ABSTRACT FROM AUTHOR]

Rock ore slice identification is a task that requires a high level of expertise. Manual identification often results in unavoidable subjective errors and is highly inefficient. Deep learning image recognition technology can efficiently perform rock ore slice identification, but training deep learning models requires a large amount of annotated data. Therefore, it is important to find efficient ways to utilize limited annotated data. By adopting a multi-label classification approach, a classifier can be trained on a labeled dataset, and then this classifier is used to generate pseudo-labels for a large number of unlabeled rock ore slice images. Finally, the model is retrained using the labeled training data and all the unlabeled data. The results show that the use of multi-label classification approach for identification of rock ore slice structures and minerals is feasible. Additionally, this paper employs a semi-supervised learning approach to train the model and improve the model’s generalization ability without requiring a large amount of manual annotation. [ABSTRACT FROM AUTHOR]

Emotion recognition has become important for easier and more effective interaction between humans and computers. It could be possible for machines to enhance and improve human communication by better understanding the variety of emotions. Therefore, constructing an emotion-specific brain-computer interface based on EEG might be the first step in overcoming that limitation by producing a neuroscientific medical tool to help such patients regain or maintain a good quality of life. Here, we used the Wavelets Multi-resolution Analysis method to extract various wavelet features from the EEG signal. Different mother wavelet functions like db, sym, coil, and haar are used to decompose EEG signals. Diverse features for five frequency bands are used, such as Power, Relative Power and Power Spectral Density, Wavelet Entropy, and statistical parameters. It has been observed that "Db6," with six decomposition levels, which yields five separate frequency bands, gives good classification accuracy. It has been observed that electrodes T7, T8, CP1, CP6, F3, F4, FP1, FP2, POZ, and F3 gives good classification accuracy. The performance is tested using various classifiers like SVM, k-NN, DT and RF. It has been observed that time domain Hjorth parameters alone have good classification accuracy compared to other features. Maximum classification accuracy of 73.42%, 71.25%, and 67.84% is achieved for Positive, Negative, and Neutral emotion using the k-NN algorithm using FP1, FP2, F3, F4, T7, T8, CP1, CP6, and POZ channels. [ABSTRACT FROM AUTHOR]

Facial features play a vital role in the real-time cloud-based applications. Since, most of the conventional models are difficult to detect heterogeneous facial features due to high computational memory and time for the internet of things (IoT) based video surveillance mechanisms. Video based facial features identification and extraction include a large number of candidates features which are difficult to detect the contextual similarity of the facial key points due to noise and computational memory. In order to resolve these issues, a hybrid multiple features extraction measures are implemented on the real-time video dataset to extract key points using the cloud-based classifier. In this work, a hybrid classifier is used to classify the key facial points in the cloud computing environment. Experimental results show that the proposed hybrid multiple feature extraction-based frameworks have better computational efficiency in terms of error rate, recall, precision, and accuracy than the conventional models. [ABSTRACT FROM AUTHOR]

Hierarchical, descriptive, and faceted methods of constructing knowledge classifiers and also the classification of knowledge using folksnomies are described. Mathematical models to formalize the listed methods of constructing classifiers are presented. A general description of the classifiers of the Russian Science Citation Index, Code of State Categories Scientific and Technical Information, and Universal Decimal Classification is given. The Chinese experience of classifying scientific publications and the American experience of classifying patent documents are analyzed. An alternative possibility for classifying scientific publications using clustering with bibliometric indicators and using keywords is indicated. A review of the main methods and means of comparing knowledge classifiers using qualifiers and expert competencies is carried out. A new approach to the compilation of knowledge classifiers based on their reduction to oriented trees and the construction of homomorphisms between these graphs is proposed. [ABSTRACT FROM AUTHOR]

Urban Functional Zones (UFZs) serve as the fundamental units of cities, making the classification and recognition of UFZs of paramount importance for urban planning and development. These differences between UFZs not only encompass geographical landscape disparities but also incorporate socio-economic information. Therefore, it is essential to extract high-precision two-dimensional (2D) and three-dimensional (3D) Urban Morphological Parameters (UMPs) and integrate socio-economic data for UFZ classification. In this study, we conducted UFZ classification using airborne LiDAR point clouds, aerial images, and point-of-interest (POI) data. Initially, we fused LiDAR and image data to obtain high-precision land cover distributions, building height models, and canopy height models, which served as accurate data sources for extracting 2D and 3D UMPs. Subsequently, we segmented city blocks based on road network data and extracted 2D UMPs, 3D UMPs, and POI Kernel Density Features (KDFs) for each city block. We designed six classification experiments based on features from single and multiple data sources. K-Nearest Neighbors (KNNs), random forest (RF), and eXtreme Gradient Boosting (XGBoost) were employed to classify UFZs. Furthermore, to address the potential data redundancy stemming from numerous input features, we implemented a feature optimization experiment. The results indicate that the experiment, which combined POI KDFs and 2D and 3D UMPs, achieved the highest classification accuracy. Three classifiers consistently exhibited superior performance, manifesting a substantial improvement in the best Overall Accuracy (OA) that ranged between 8.31% and 17.1% when compared to experiments that relied on single data sources. Among these, XGBoost outperformed the others with an OA of 84.56% and a kappa coefficient of 0.82. By conducting feature optimization on all 107 input features, the classification accuracy of all three classifiers exceeded 80%. Specifically, the OA for KNN improved by 10.46%. XGBoost maintained its leading performance, achieving an OA of 86.22% and a kappa coefficient of 0.84. An analysis of the variable importance proportion of 24 optimized features revealed the following order: 2D UMPs (46.46%) > 3D UMPs (32.51%) > POI KDFs (21.04%). This suggests that 2D UMPs contributed the most to classification, while a ranking of feature importance positions 3D UMPs in the lead, followed by 2D UMPs and POI KDFs. This highlights the critical role of 3D UMPs in classification, but it also emphasizes that the socio-economic information reflected by POI KDFs was essential for UFZ classification. Our research outcomes provide valuable insights for the rational planning and development of various UFZs in medium-sized cities, contributing to the overall functionality and quality of life for residents. [ABSTRACT FROM AUTHOR]

Conventionally, fine-tuning or transfer learning using a pre-trained convolutional network is adopted to design a classifier. However, when the dataset is small this can deteriorate the classifier generalization performance due to negative transfer or overfitting issues. In this paper, we suggest a flexible and high-capacity multiple kernel-based convolutional neural network (MK-CNN) to automate the pathological brain classification task. The proposed network employed different stacks of convolution with various kernels to obtain multi-scale features from the input image. The smaller kernel size provides specific information about the local features whereas the larger kernel size provides the global spatial information. Hence, the network takes into account both regional specifics and global spatial consistency thanks to this multi-scale methodology. Only the output layer is shared between each network stack. This makes it possible to specifically tweak the CNN's weights and biases for each convolution stack and associated kernel size. The results reported on real patient data from the Harvard Whole Brain Atlas reveal that our method outperforms state-of-the-art techniques. The suggested approach may be used to help experts carry out the clinical follow-up study. [ABSTRACT FROM AUTHOR]

Feature selection is becoming a relevant problem within the field of machine learning. The feature selection problem focuses on the selection of the small, necessary, and sufficient subset of features that represent the general set of features, eliminating redundant and irrelevant information. Given the importance of the topic, in recent years there has been a boom in the study of the problem, generating a large number of related investigations. Given this, this work analyzes 161 articles published between 2019 and 2023 (20 April 2023), emphasizing the formulation of the problem and performance measures, and proposing classifications for the objective functions and evaluation metrics. Furthermore, an in-depth description and analysis of metaheuristics, benchmark datasets, and practical real-world applications are presented. Finally, in light of recent advances, this review paper provides future research opportunities. [ABSTRACT FROM AUTHOR]

Classifiers are used for categorising nouns according to animacy, form, shape, and size. As one of the Austronesian languages, Malay is said to have an extensive numeral classifier system. Past studies have classified these two classifiers as general classifiers; however, an extensive study comparing the use of these two classifiers have not been attempted. This study aims to highlight the similarities and differences by comparing the Chinese classifier “个 (gè)” and the Malay “buah” using evidence from reference classifier dictionaries of the two languages. The findings show that while some similarities exist and both classifiers can be used interchangeably with some nouns, these classifiers are unique and have distinctive semantic usage. Both 个 (gè) and buah are used for organisation, products of nature, buildings and places, and artifacts (things created by humans). However, only 个 (gè) can be used for organs or body parts, humans, fruits and food, time, directions, and thought and intellectual products. In contrast, only buah is used for transportation, accessories, home furniture and appliances, publications, musical instruments, and performances. [ABSTRACT FROM AUTHOR]

Proteins are played key roles in different functionalities in our daily life. All functional roles of a protein are a bit enhanced in interaction compared to individuals. Identification of essential proteins of an organism is a time consume and costly task during observation in the wet lab. The results of observation in wet lab always ensure high reliability and accuracy in the biological ground. Essential protein prediction using computational approaches is an alternative choice in research. It proves its significance rapidly in day‐to‐day life as well as reduces the experimental cost of wet lab effectively. Existing computational methods were implemented using Protein interaction networks (PPIN), Sequence, Gene Expression Dataset (GED), Gene Ontology (GO), Orthologous groups, and Subcellular localized datasets. Machine learning has diverse categories of features that enable to model and predict essential macromolecules of understudied organisms. A novel methodology MEM‐FET (membership feature) is predicted based on features, that is, edge clustering coefficient, Average clustering coefficient, subcellular localization, and Gene Ontology within a compartment of common neighbors. The accuracy (ACC) values of the predicted true positive (TP) essential proteins are 0.79, 0.74, 0.78, and 0.71 for YHQ, YMIPS, YDIP, and YMBD datasets. An enriched set of essential proteins are also predicted using the MEM‐FET algorithm. Ensemble ML also validated the proposed model with an accuracy of 60%. It has been predicted that MEM‐FET algorithms outperform other existing algorithms with an ACC value of 80% for the yeast dataset. [ABSTRACT FROM AUTHOR]

Background and Objectives: Considering the drought and global warming, it is very important to monitor changes in water bodies for surface water management and preserve water resources in the natural ecosystem. For this purpose, using the appropriate spectral indices has high capabilities to distinguish surface water bodies from other land covers. This research has a special consideration to the effect of different types of land covers around water bodies. For this reason, two different water bodies, lake and wetland, have been used to evaluate the implementation results. Methods: The main objective of this research is to evaluate the capabilities of the genetic algorithm in optimum selection of the spectral indices extracted from Sentinel-2 satellite image due to distinguish surface water bodies in two case studies: 1) the pure water behind the Karkheh dam and 2) the Shadegan wetland having water mixed with vegetation. In this regard, the set of optimal indices is obtained with the genetic algorithm followed by the support vector machine (SVM) classifier. Results: The evaluation of the classification results based on the optimum selected spectral indices showed that the overall accuracy and Kappa coefficient of the recognized surface water bodies are 98.18 and 0.9827 in the Karkheh dam and 98.04 and 0.93 in Shadegan wetland, respectively. Evaluation of each of the spectral indices measured in both study areas was carried out using quantitative decision tree (DT) classifier. The best obtained DT classification results show the improvements in overall accuracy by 1.42% in the Karkheh Dam area and 1.56% in the Shadegan Wetland area based on the optimum selected indices by genetic algorithm followed by SVM classifier. Moreover, the obtained classification results are superior compared with Random Forest classifier using the optimized set of spectral features. Conclusion: Applying the genetic algorithm on the spectral indices was able to obtain two optimal sets of effective indices that have the highest amount of accuracy in classifying water bodies from other land cover objects in the study areas. Considering the collective performance, genetic algorithm selects an optimal set of indices that can detect water bodies more accurately than any single index. [ABSTRACT FROM AUTHOR]

The continuous advancement of computer networks has given rise to grave concerns regarding security and susceptibility. Network administrators utilize intrusion detection systems (IDS) to deliver essential network security. Current IDS devices produce false alarms in response to routine user activities rather than detecting novel assaults. Neural networks may be used to overcome this problem and increase detection accuracy. In this paper, we propose a hybrid approach based on neural networks and correlation-based feature selection to detect anomalies. Experimental research is done on the standard dataset NSL-KDD for intrusion detection using current attacks. We introduce a novel hybrid crowd search analysis optimization with an artificial neural network (HCSAOANN). The findings demonstrate that it outperforms in high accuracy, precision, F1-Score, and recall. In the proposed HCSAOANN algorithm, to explore the feature space, we merged the crow search optimization (CSO), which can converge into the overall best solution in the search function, with the upgraded version of the crow search analysis method. Some performance criteria were applied in the studies using an artificial neural network (ANN) as a classifier. The HCSAOANN methodology outperformed as compared to the previous fuzzy technique and achieved 98% accuracy and a 98% precision rate, which is 2.2% better than the previous CSO-ANFIS technique and 8.87% superior to the FC-ANN technique. [ABSTRACT FROM AUTHOR]

Background: Pancreatic cancer is characterized by an extremely poor prognosis, even following potentially curative resection. Classical prognostic markers such as histopathological or clinical parameters have limited predictive power. The present study aimed to establish a prognostic model combining mRNA expression data with histopathological and clinical data to better predict survival and stratify pancreatic cancer patients following resection. We pioneered three models in one study and systematically evaluated the clinical benefits of all three models. Methods: To identify differentially expressed genes in pancreatic cancer, mRNA data from normal (GTEx database) and pancreatic cancer (TCGA database) tissues were used. Survival analysis was carried out to identify prognosis-relevant genes from the identified differentially expressed genes and LASSO regression was used to filter out hub genes. The risk score of several hub genes was calculated according to gene expression and coefficients. Validation was carried out using an independent set of GEO microarray data. Multivariate COX regression was used for identifying independent clinical and pathological risk factors related to patient's survival in the TCGA database and a prognostic model combining mRNA expression data with histopathological and clinical data was established. Another prognostic model using clinicopathological factors from the SEER database was conceived based on multivariate COX regression. NRI (net reclassification improvement) and IDI (integrated discrimination index) were used to compare the predictive capabilities of the different models. Results: We identified 1589 differentially expressed genes (DEGs) through the comparison of normal and pancreatic cancer tissues, of whom 317 were associated with prognosis(p

Accurate estimation of state-of-charge (SoC) is significant for monitoring the operation of LiFePO4 batteries. This paper addresses the challenges posed by the nonlinear characteristics of LiFePO4 batteries, which adversely affect the accuracy of SoC estimation. The proposed novel methodology for SoC estimation in LiFePO4 batteries involves a Support Vector Machine (SVM) assisted Artificial Neural Network (ANN) model incorporating principal component analysis (PCA) to efficiently interpret the input data. To improve the prediction accuracy, the non-linear characteristics of LiFePO4 are divided into three parts and each of these parts is used to train a separate ANN model. Further, an optimal number of hidden layers and neurons are selected for each ANN model to minimize the prediction errors. The usage of dedicated smaller datasets for each ANN model simplifies the structure. SVM classifies the battery operating regions and selects the most suitable ANN model for SoC estimation. PCA is applied to process the obtained experimental data resulting in three principal components serving as inputs for the SVM-assisted ANN model. Further, with PCA the input dimensions are reduced from four to three, thereby leading to computational simplicity. The input data comprises of current, voltage, open-circuit voltage and temperature of the battery. An experimental prototype, comprising a customized battery pack and sensing mechanisms, is developed for data collection for training SVM and ANN models. With the proposed SVM-assisted ANN involving PCA, the loss function is minimized and an average Root Mean Square Error (RMSE) of 0.3133 is achieved. This demonstrates the feasibility, accuracy and applicability of SoC estimation with the developed SVM-assisted ANN model for LiFePO4 batteries.

In the rapid development of information technology in the field of Building Information Modeling (BIM) there is a growing need for efficient classification of construction information. One of the key steps to move towards digital construction involves creating reliable systems for classifying BIM elements, providing the foundation for various use cases, from facilitating model navigation to obtaining practical outcomes such as cost estimates and materials quantities. However, the BIM classification process in practice is labor-intensive and time-consuming and leads to an increase in the cost. This study explores the application of an innovative method, based on artificial intelligence algorithms. This method automates the assignment of codes to information model components. The research investigates classification systems, machine learning models and selects the most accurate one for the classification task. It is based on metrics such as accuracy and F1-score in order to achieve an optimal balance between the efficiency and accuracy according to predefined parameters. The article presents software for automatic prediction and assignment of codes in accordance with the selected classifier, developed on selected algorithms.

Recently, there have been notable advancements in video editing software. These advancements have allowed novices or those without access to advanced computer technology to generate videos that are visually indistinguishable to the human eye from real ones to the human observer. Therefore, the application of deepfake technology has the potential to expand the scope of identity theft, which poses a significant risk and a formidable challenge to global security. The development of an effective approach for detecting fake videos is necessary. Here, we introduce a novel methodology that employs a convolutional neural network (CNN) and Gaussian mixture model (GMM) to effectively differentiate between fake and real images or videos. The proposed methodology presents a novel CNN-GMM architecture in which the fully connected (FC) layer in the CNN is replaced with a customized Gaussian mixture model (GMM) fully connected layer. The GMM layer utilizes a weighted set of Gaussian probability density functions (PDFs) to represent the distribution of data frequencies in both real and fake images. This representation indicates there is a shift in the distribution of the manipulated images due to added noise. The CNN-GMM model demonstrates the ability to accurately identify variations resulting from different types of deepfakes within the probability distribution. It achieves a high level of classification accuracy, reaching up to 100% in training accuracy and up to 96% in validation accuracy. Notwithstanding the ratio of the genuine class to the counterfeit class being 16.6% to 83.4%, the CNN-GMM model exhibited high-performance metrics in terms of recall, accuracy, and F-score when classifying the least genuine class.

Background: Medical image segmentation is a vital yet difficult job because of the multimodality of the acquired images. It is difficult to locate the polluted area before it spreads. Methods: This research makes use of several machine learning tools, including an artificial neural network as well as a random forest classifier, to increase the system's reliability of pulmonary nodule classification. Anisotropic diffusion filtering is initially used to remove noise from a picture. After that, a modified random walk method is used to get the region of interest inside the lung parenchyma. Finally, the features corresponding to the consistency of the picture segments are extracted using texture-based feature extraction for pulmonary nodules. The final stage is to identify and classify the pulmonary nodules using a classifier algorithm. Results: The studies employ cross-validation to demonstrate the validity of the diagnosis framework. In this instance, the proposed method is tested using CT scan information provided by the Lung Image Database Consortium. A random forest classifier showed 99.6 percent accuracy rate for detecting lung cancer, compared to a artificial neural network's 94.8 percent accuracy rate. Conclusions: Due to this, current research is now primarily concerned with identifying lung nodules and classifying them as benign or malignant. The diagnostic potential of machine learning as well as image processing approaches are enormous for the categorization of lung cancer.

Study region: Yangtze River Delta core urban agglomeration, China Study focus: Traditional research on flood susceptibility assessment using machine learning often seeks to enhance model performance by increasing the number of input variables, which is impractical in regions with limited data availability. In this study, we constructed a variable system comprising 13 features for flood susceptibility assessment through machine learning techniques. A flexible framework, primarily incorporating methods for importance value calculation and repeated random sampling, were established to identify a minimal set of features that yield high-performance classifiers. Finally, the feasibility of the proposed framework was verified by comparing the classifier performances and flood susceptibility maps. New hydrological insights for the region: Results underscored the significance of features such as Land Use / Land Cover, Impervious Area, Normalized Difference Vegetation Index, Distance to Lake and Built-up Probability in model development. These five features proved sufficient to produce a classifier with Area Under the Curve (AUC) indices exceeding 0.9 for both training and testing data. Susceptibility maps generated using varying feature counts revealed that regions with limited vegetation cover and near lakes face higher flood susceptibility. The framework's feasibility and viability were confirmed by the excellent classifier performance (mean AUC > 0.9) with reduced features and the consistent outcomes of generated maps, offering theoretical and technical support for flooding research in data-constrained regions.

Abstract Background Autism Spectrum Disorder (ASD) diagnosis can be aided by approaches based on eye-tracking signals. Recently, the feasibility of building Visual Attention Models (VAMs) from features extracted from visual stimuli and their use for classifying cases and controls has been demonstrated using Neural Networks and Support Vector Machines. The present work has three aims: 1) to evaluate whether the trained classifier from the previous study was generalist enough to classify new samples with a new stimulus; 2) to replicate the previously approach to train a new classifier with a new dataset; 3) to evaluate the performance of classifiers obtained by a new classification algorithm (Random Forest) using the previous and the current datasets. Methods The previously approach was replicated with a new stimulus and new sample, 44 from the Typical Development group and 33 from the ASD group. After the replication, Random Forest classifier was tested to substitute Neural Networks algorithm. Results The test with the trained classifier reached an AUC of 0.56, suggesting that the trained classifier requires retraining of the VAMs when changing the stimulus. The replication results reached an AUC of 0.71, indicating the potential of generalization of the approach for aiding ASD diagnosis, as long as the stimulus is similar to the originally proposed. The results achieved with Random Forest were superior to those achieved with the original approach, with an average AUC of 0.95 for the previous dataset and 0.74 for the new dataset. Conclusion In summary, the results of the replication experiment were satisfactory, which suggests the robustness of the approach and the VAM-based approaches feasibility to aid in ASD diagnosis. The proposed method change improved the classification performance. Some limitations are discussed and additional studies are encouraged to test other conditions and scenarios.