Baseline Classifier Research Articles

The IBEM dataset: A large printed scientific image dataset for indexing and searching mathematical expressions

Searching for information in printed scientific documents is a challenging problem that has recently received special attention from the Pattern Recognition research community. Mathematical expressions are complex elements that appear in scientific documents, and developing techniques for locating and recognizing them requires the preparation of datasets that can be used as benchmarks. Most current techniques for dealing with mathematical expressions are based on Machine Learning techniques which require a large amount of annotated data. These datasets must be prepared with ground-truth information for automatic training and testing. However, preparing large datasets with ground-truth is a very expensive and time-consuming task. This paper introduces the IBEM dataset, consisting of scientific documents that have been prepared for mathematical expression recognition and searching. This dataset consists of 600 documents, more than 8200 page images with more than 160000 mathematical expressions. It has been automatically generated from the ▪ version of the documents and can be enlarged easily. The ground-truth includes the position at the page level and the ▪ transcript for mathematical expressions both embedded in the text and displayed. This paper also reports a baseline classification experiment with mathematical symbols and a baseline experiment of Mathematical Expression Recognition performed on the IBEM dataset. These experiments aim to provide some benchmarks for comparison purposes so that future users of the IBEM dataset can have a baseline framework.

Phishing Node Detection in Ethereum Transaction Network Using Graph Convolutional Networks

As the use of digital currencies, such as cryptocurrencies, increases in popularity, phishing scams and other cybercriminal activities on blockchain platforms (e.g., Ethereum) have also risen. Current methods of detecting phishing in Ethereum focus mainly on the transaction features and local network structure. However, these methods fail to account for the complexity of interactions between edges and the handling of large graphs. Additionally, these methods face significant issues due to the limited number of positive labels available. Given this, we propose a scheme that we refer to as the Bagging Multiedge Graph Convolutional Network to detect phishing scams on Ethereum. First, we extract the features from transactions and transform the complex Ethereum transaction network into three simple inter-node graphs. Then, we use graph convolution to generate node embeddings that leverage the global structural information of the inter-node graphs. Further, we apply the bagging strategy to overcome the issues of data imbalance and the Positive Unlabeled (PU) problem in transaction data. Finally, to evaluate our approach’s effectiveness, we conduct experiments using actual transaction data. The results demonstrate that our Bagging Multiedge Graph Convolutional Network (0.877 AUC) outperforms all of the baseline classification methods in detecting phishing scams on Ethereum.

Deep learning-based classification system of bacterial keratitis and fungal keratitis using anterior segment images.

Infectious keratitis is a vision threatening disease. Bacterial and fungal keratitis are often confused in the early stages, so right diagnosis and optimized treatment for causative organisms is crucial. Antibacterial and antifungal medications are completely different, and the prognosis for fungal keratitis is even much worse. Since the identification of microorganisms takes a long time, empirical treatment must be started according to the appearance of the lesion before an accurate diagnosis. Thus, we developed an automated deep learning (DL) based diagnostic system of bacterial and fungal keratitis based on the anterior segment photographs using two proposed modules, Lesion Guiding Module (LGM) and Mask Adjusting Module (MAM). We used 684 anterior segment photographs from 107 patients confirmed as bacterial or fungal keratitis by corneal scraping culture. Both broad- and slit-beam images were included in the analysis. We set baseline classifier as ResNet-50. The LGM was designed to learn the location information of lesions annotated by ophthalmologists and the slit-beam MAM was applied to extract the correct feature points from two different images (broad- and slit-beam) during the training phase. Our algorithm was then externally validated using 98 images from Google image search and ophthalmology textbooks. A total of 594 images from 88 patients were used for training, and 90 images from 19 patients were used for test. Compared to the diagnostic accuracy of baseline network ResNet-50, the proposed method with LGM and MAM showed significantly higher accuracy (81.1 vs. 87.8%). We further observed that the model achieved significant improvement on diagnostic performance using open-source dataset (64.2 vs. 71.4%). LGM and MAM module showed positive effect on an ablation study. This study demonstrated that the potential of a novel DL based diagnostic algorithm for bacterial and fungal keratitis using two types of anterior segment photographs. The proposed network containing LGM and slit-beam MAM is robust in improving the diagnostic accuracy and overcoming the limitations of small training data and multi type of images.

Classification of Alzheimer's using Deep-learning Methods on Webcam-based Gaze Data

There has been increasing interest in non-invasive predictors of Alzheimer's disease (AD) as an initial screen for this condition. Previously, successful attempts leveraged eye-tracking and language data generated during picture narration and reading tasks. These results were obtained with high-end, expensive eye-trackers. Instead, we explore classification using eye-tracking data collected with a webcam, where our classifiers are built using a deep-learning approach. Our results show that the webcam gaze classifier is not as good as the classifier based on high-end eye-tracking data. However, the webcam-based classifier still beats the majority-class baseline classifier in terms of AU-ROC, indicating that predictive signals can be extracted from webcam gaze tracking. Hence, although our results indicate that there is still a long way to go before webcam gaze tracking can reach practical relevance, they still provide an encouraging proof of concept that this technology should be further explored as an affordable alternative to high-end eye-trackers for the detection of AD.

Leukocytes Classification for Leukemia Detection Using Quantum Inspired Deep Feature Selection.

Leukocytes, also referred to as white blood cells (WBCs), are a crucial component of the human immune system. Abnormal proliferation of leukocytes in the bone marrow leads to leukemia, a fatal blood cancer. Classification of various subtypes of WBCs is an important step in the diagnosis of leukemia. The method of automated classification of WBCs using deep convolutional neural networks is promising to achieve a significant level of accuracy, but suffers from high computational costs due to very large feature sets. Dimensionality reduction through intelligent feature selection is essential to improve the model performance with reduced computational complexity. This work proposed an improved pipeline for subtype classification of WBCs that relies on transfer learning for feature extraction using deep neural networks, followed by a wrapper feature selection approach based on a customized quantum-inspired evolutionary algorithm (QIEA). This algorithm, inspired by the principles of quantum physics, outperforms classical evolutionary algorithms in the exploration of search space. The reduced feature vector obtained from QIEA was then classified with multiple baseline classifiers. In order to validate the proposed methodology, a public dataset of 5000 images of five subtypes of WBCs was used. The proposed system achieves a classification accuracy of about 99% with a reduction of 90% in the size of the feature vector. The proposed feature selection method also shows a better convergence performance as compared to the classical genetic algorithm and a comparable performance to several existing works.

Cell-type classification of cancer single-cell RNA-seq data using the Subsemble ensemble-based machine learning classifier

Background The advent of single-cell RNA sequencing (scRNA-seq) has provided a high-resolution overview of the cellular heterogeneity of different tissue types. Manual cell type annotation of gene expression datasets remains a useful but time-intensive task. Ensemble machine learning methods leverage the predictive power of multiple classifiers and can be applied to classify high-dimensional gene expression data. Here, we present a novel application of the Subsemble supervised ensemble machine learning classifier used to classify novel cells with known cell type labels using gene expression data. Methods First, we tested the classification performance of different pre-processing steps used to normalize and upsample the training dataset for the Subsemble using a colorectal cancer dataset. Second, we conducted a cross-validated performance benchmark of the Subsemble classifier compared to nine other cell type classification methods across five metrics tested, using an acute myeloid leukemia dataset. Third, we conducted a comparative performance benchmark of the Subsemble classifier using a patient-based leave-one-out cross-validation scheme. Rank normalized scores were calculated for each classifier to aggregate performance across multiple metrics. Results The Subsemble classifier performed best when trained on a dataset that was log-transformed then upsampled to generate balanced class distributions. The Subsemble classifier was consistently the top-ranked classifier across five classification performance metrics compared to the nine other baseline classifiers and showed an improvement in performance as the training dataset increased. When tested using the patient-based leave-one-out cross-validation scheme, the Subsemble was the top-ranked classifier based on rank normalized scores. Conclusions Our proof-of-concept study showed that the Subsemble classifier can be used to accurately predict known cell type labels from single-cell gene expression data. The top-ranked classification performance of the Subsemble across two validation datasets, two cross-validation schemes, and five performance metrics motivates future development of accurate ensemble classifiers of scRNA-seq datasets.

A blockchain-enabled deep residual architecture for accountable, in-situ quality control in industry 4.0 with minimal latency

Real-time and vision-based quality control for industrial processes has drawn great interest from both scientists and practitioners, particularly following the transition to Zero Defect Manufacturing (ZDM) and Industry 4.0. Despite considerable progress, most ZDM approaches focus on the accuracy of the inspection process, often neglecting critical factors for application in the shop floor. On one hand, near real-time methods are needed for early defect detection and containment. On the other hand, data scarcity is an issue causing AI methods to overfit. Another concern is the accountability of AI results, since even if an AI pipeline is successfully deployed, its predictions are not verifiable in the long term. In this work, we explore a real-time solution based on lightweight Deep Residual Networks and Blockchain technology to address these issues. Concretely, we propose a two-phase training strategy to boost the performance of baseline classifiers while maintaining low inference times. The performance of the proposed methodology is presented in two different industrial use cases with strict timing requirements, one concerning battery assembly line and the other antenna manufacturing. We validate the proposed method for defect detection and compare the results with common training strategies demonstrating an improvement of 3% and 10% in F1-score and accuracy on the two cases respectively, while lowering inference time by 2.2× compared to existing light architectures. Contributing to the accountability of AI results, we present an IoT framework using Blockchain deployed in Private Ethereum.

Improving the workflow to crack Small, Unbalanced, Noisy, but Genuine (SUNG) datasets in bioacoustics: The case of bonobo calls.

Despite the accumulation of data and studies, deciphering animal vocal communication remains challenging. In most cases, researchers must deal with the sparse recordings composing Small, Unbalanced, Noisy, but Genuine (SUNG) datasets. SUNG datasets are characterized by a limited number of recordings, most often noisy, and unbalanced in number between the individuals or categories of vocalizations. SUNG datasets therefore offer a valuable but inevitably distorted vision of communication systems. Adopting the best practices in their analysis is essential to effectively extract the available information and draw reliable conclusions. Here we show that the most recent advances in machine learning applied to a SUNG dataset succeed in unraveling the complex vocal repertoire of the bonobo, and we propose a workflow that can be effective with other animal species. We implement acoustic parameterization in three feature spaces and run a Supervised Uniform Manifold Approximation and Projection (S-UMAP) to evaluate how call types and individual signatures cluster in the bonobo acoustic space. We then implement three classification algorithms (Support Vector Machine, xgboost, neural networks) and their combination to explore the structure and variability of bonobo calls, as well as the robustness of the individual signature they encode. We underscore how classification performance is affected by the feature set and identify the most informative features. In addition, we highlight the need to address data leakage in the evaluation of classification performance to avoid misleading interpretations. Our results lead to identifying several practical approaches that are generalizable to any other animal communication system. To improve the reliability and replicability of vocal communication studies with SUNG datasets, we thus recommend: i) comparing several acoustic parameterizations; ii) visualizing the dataset with supervised UMAP to examine the species acoustic space; iii) adopting Support Vector Machines as the baseline classification approach; iv) explicitly evaluating data leakage and possibly implementing a mitigation strategy.

Prediction for chronic kidney disease by categorical and non_categorical attributes using different machine learning algorithms.

Chronic kidney disease (CKD) is a common disease as it is difficult to diagnose early due to its lack of symptoms. The main goal is to first diagnose kidney failure, which is a requirement for dialysis or a kidney transplant. This model teaches patients how to live a healthy life, helps doctors identify the risk and severity of disease, and how plan future treatments. Machine learning algorithms are often used in health care to predict and manage the disease. The purpose of this study is to develop a model for the early detection of CKD, which has three parts: (a) applying baseline classifiers on categorical attributes, (b) applying baseline classifiers on non_categorical attributes, (c) applying baseline classifiers on both categorical and non_categorical attributes, and (d) improving the results of the proposed model by combing the results of above three classifiers based on a majority vote. The proposed model based on baseline classifiers and the majority voting method shows a 3% increase in accuracy over the other existing models. The results provide support for increased accuracy in the current classification of chronic kidney disease.

An Artificial Intelligence model for smart post-stroke assessment using wearable sensors

Wearable sensors provide a recording capability of the body segments’ motion. By collecting and analyzing the segments’ movements, automating the post-stroke assessment process would be feasible and reliable. Post-stroke classification of impaired or non-impaired body parts is the first step of automating the assessment process. It is recently achievable by utilizing enhancements in supervised machine learning (ML) using wearable sensors. Many classification-based approaches have numerous complexity/quality trade-offs with no optimal solution for all datasets of various structures, properties, distributions, and sizes. Ensemble machine learning provides reliable classification systems for diagnostic purposes. However, there are no criteria for identifying the baseline models in the aggregate (parallel or sequential aggregation), which significantly impacts the complexity and accuracy of the designed ensemble. In this paper, we propose an efficient post-stroke diagnosis system using the notion of multi-level ensemble learning, the Multi-Level Meta Learner (MLML) ensemble algorithm with heterogeneous or homogeneous baseline classifiers using Xsens wearable sensor collected dataset. The MLML outperforms the baseline models with comparable performance while generating a list of optimal candidate heterogeneous baseline classifiers. This list can provide only potential positive combinations, significantly improving the performance and reducing the complexity of building an ensemble. The linear acceleration and angular velocity derived from the wearable sensors are utilized. Experimental results show that the MLML has enhanced the accuracy of classifying the impaired hands from non-impaired hands with an improvement of up to 95.64% and 91.2% for boosting and Bagging learning, respectively.

Abstract 3270: The XernaTM TME Panel potentially predicts response to a combination of the TLR9 agonist vidutolimod and PD-1 inhibitor pembrolizumab in metastatic melanomas with prior anti-PD-1 treatment

Abstract Background: Few therapeutic options exist for anti-PD-1 refractory, metastatic melanoma patients, and today’s biomarkers are insufficient to aid in defining who should receive potential combinatorial immunotherapies. Results from a phase Ib, multicenter study (NCT02680184) showed that a combination of vidutolimod and pembrolizumab provided a best overall response of 23.5% in patients with metastatic or unresectable cutaneous melanoma who had received prior anti-PD-1 therapy. The Xerna™ TME Panel consists of an artificial neural net that utilizes the expression of ~100 genes involved in angiogenesis and tumor immune biologies to classify patient samples into one of four tumor microenvironment (TME) biomarker subtypes: Angiogenesis (A), Immune Active (IA), Immune Desert (ID), or Immune Suppressed (IS). We hypothesized that the IS subtype is predictive of vidutolimod + pembrolizumab benefit in this cohort compared to the other TME subtypes (A, IA, and ID). Methods: Total RNASeq was performed on FFPE biopsies collected from a subset of patients prior to therapy (N=38) and 2 weeks post-initiation of therapy (N=10). Gene expression data was analyzed using the Xerna TME Panel algorithm to assign a TME subtype. Correlational analyses between TME subtypes, response to therapy, and other hallmark gene signatures were performed. Results: Overall response rate in the pretreatment cohort available for biomarker analysis was 26%, comparable to the entire vidutolimod/pembrolizumab arm. The cohort had a skewed distribution of TME subtypes with high prevalence of IS (34%) and ID (45%), indicative of immune therapy-refractory biologies. An overall response of 54% was observed in the IS subtype, compared with 12% in the other subtypes combined. Comparison with other “hallmark” gene signatures confirmed enrichment of immune and angiogenesis biologies in the IS subtype, but none of these individual hallmark signatures were found to differentiate between responders and non-responders. The Xerna TME Panel demonstrated superior classification performance across all criteria compared to a baseline classifier, including accuracy (0.76 vs. 0.62), sensitivity (0.70 vs. 0.27) and specificity (0.79 vs. 0.74). Among matched post-treatment samples, 70% revealed a change in TME subtype compared to their pre-treatment status. Three of the post-treatment samples represented changes from an immune-low subtype to an immune-high subtype, including one complete responder with a pre-treatment ID that changed to IA while on therapy, illustrating how the TME Panel may be used to interpret the mechanism of drug response. Conclusions: The Xerna TME Panel shows potential activity as a predictive and pharmacodynamic biomarker for the combination of vidutolimod and pembrolizumab in anti-PD-1 refractory melanoma patients. Citation Format: Mark T. Uhlik, Daniel Pointing, Luka Ausec, Miha Stajdohar, Robert Cvitkovic, Matjaz Zganec, Seema Iyer, Hong Liu, Art Krieg, Laura Benjamin. The XernaTM TME Panel potentially predicts response to a combination of the TLR9 agonist vidutolimod and PD-1 inhibitor pembrolizumab in metastatic melanomas with prior anti-PD-1 treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3270.

CPED: A Chinese Positive Emotion Database for Emotion Elicitation and Analysis

Positive emotions are of great significance to people's daily life, such as human-computer/robot interaction. However, the structure of extensive positive emotions is not clear yet and effective standardized inducing materials containing as many positive emotional categories as possible are lacking. Thus, this paper aims to establish a Chinese positive emotion database (CPED) to (1) effectively elicit positive emotion categories as many as possible, (2) provide both the subjective feelings of different positive emotions and a corresponding peripheral physiological database, and (3) explore the structure and framework of positive emotion categories. 42 video clips of 16 positive emotion categories were screened from 1000+ online clips. Then a total of 312 participants watched and rated these video clips during which GSR and PPG signals were recorded. 34 video clips that met hit rate and intensity standards were systemically clustered into four emotion categories (empathy, fun, creativity and esteem). Eventually, 22 film clips of these four major categories formed the CPED database. A total of 84 features from GSR and PPG signals were extracted and entered into RF, SVM, DBN and LSTM classifiers that serves as baseline classification methods. A classification accuracy of 44.66% for four major categories of positive emotions was achieved.

Detecting CTP truncation artifacts in acute stroke imaging from the arterial input and the vascular output functions.

Current guidelines for CT perfusion (CTP) in acute stroke suggest acquiring scans with a minimal duration of 60-70 s. But even then, CTP analysis can be affected by truncation artifacts. Conversely, shorter acquisitions are still widely used in clinical practice and may, sometimes, be sufficient to reliably estimate lesion volumes. We aim to devise an automatic method that detects scans affected by truncation artifacts. Shorter scan durations are simulated from the ISLES'18 dataset by consecutively removing the last CTP time-point until reaching a 10 s duration. For each truncated series, perfusion lesion volumes are quantified and used to label the series as unreliable if the lesion volumes considerably deviate from the original untruncated ones. Afterwards, nine features from the arterial input function (AIF) and the vascular output function (VOF) are derived and used to fit machine-learning models with the goal of detecting unreliably truncated scans. Methods are compared against a baseline classifier solely based on the scan duration, which is the current clinical standard. The ROC-AUC, precision-recall AUC and the F1-score are measured in a 5-fold cross-validation setting. The best performing classifier obtained an ROC-AUC of 0.982, precision-recall AUC of 0.985 and F1-score of 0.938. The most important feature was the AIFcoverage, measured as the time difference between the scan duration and the AIF peak. When using the AIFcoverage to build a single feature classifier, an ROC-AUC of 0.981, precision-recall AUC of 0.984 and F1-score of 0.932 were obtained. In comparison, the baseline classifier obtained an ROC-AUC of 0.954, precision-recall AUC of 0.958 and F1-Score of 0.875. Machine learning models fed with AIF and VOF features accurately detected unreliable stroke lesion measurements due to insufficient acquisition duration. The AIFcoverage was the most predictive feature of truncation and identified unreliable short scans almost as good as machine learning. We conclude that AIF/VOF based classifiers are more accurate than the scans' duration for detecting truncation. These methods could be transferred to perfusion analysis software in order to increase the interpretability of CTP outputs.

Impact of localized fine tuning in the performance of segmentation and classification of lung nodules from computed tomography scans using deep learning.

Algorithm malfunction may occur when there is a performance mismatch between the dataset with which it was developed and the dataset on which it was deployed. A baseline segmentation algorithm and a baseline classification algorithm were developed using public dataset of Lung Image Database Consortium to detect benign and malignant nodules, and two additional external datasets (i.e., HB and XZ) including 542 cases and 486 cases were involved for the independent validation of these two algorithms. To explore the impact of localized fine tuning on the individual segmentation and classification process, the baseline algorithms were fine tuned with CT scans of HB and XZ datasets, respectively, and the performance of the fine tuned algorithms was tested to compare with the baseline algorithms. The proposed baseline algorithms of both segmentation and classification experienced a drop when directly deployed in external HB and XZ datasets. Comparing with the baseline validation results in nodule segmentation, the fine tuned segmentation algorithm obtained better performance in Dice coefficient, Intersection over Union, and Average Surface Distance in HB dataset (0.593 vs. 0.444; 0.450 vs. 0.348; 0.283 vs. 0.304) and XZ dataset (0.601 vs. 0.486; 0.482 vs. 0.378; 0.225 vs. 0.358). Similarly, comparing with the baseline validation results in benign and malignant nodule classification, the fine tuned classification algorithm had improved area under the receiver operating characteristic curve value, accuracy, and F1 score in HB dataset (0.851 vs. 0.812; 0.813 vs. 0.769; 0.852 vs. 0.822) and XZ dataset (0.724 vs. 0.668; 0.696 vs. 0.617; 0.737 vs. 0.668). The external validation performance of localized fine tuned algorithms outperformed the baseline algorithms in both segmentation process and classification process, which showed that localized fine tuning may be an effective way to enable a baseline algorithm generalize to site-specific use.

Biomarker Based Detection and Staging of Breast Cancer from Blood Using Raman Spectroscopy and Deep Learning Technique

Breast cancer (BC) or breast neoplasm is causing major menace to the life of women around the world. The significance of early detection and staging of BC has been substantial in diagnosing protocol. This work aims to develop an automated system that combines multivariate data analysis (PCA - principal components analysis) with ensemble recurrent neural network models (stacked OGRU-LSTM) to identify Raman spectral characteristics that can be used as spectral cancer markers for the detection of BC progression and staging. Features of blood plasma from histopathologically diagnosed BC candidates were compared to healthy ones in this study. The same is performed on different leading classification models as the stacked basic RNN, the stacked-RNN-LSTM, and RNN-GRU models. A total of 2,340 Raman spectra generated is evaluated in this study. It is found from the study that stage 3 and stage 2 are structurally identical, but with PCA-Factorial Discriminant Analysis (FDA) they can be distinguished from each other, hence the Raman spectrum pertaining to blood plasma samples of the BC candidates is classified efficiently, yielding potentially high values of specificity and sensitivity for all the BC stages. Comparative classification results show that the stacked OGRU-LSTM model outperforms well for BC detection, and better differentiates various stages of BC by employing the multivariate data analysis technique. The stacked OGRU-LSTM model achieved the highest classification accuracy (97.89 %), Cohen-kappa score (0.928), F1-score (0.957), and the lowermost number of test loss and MSE (0.037), indicating that the model outperforms other baseline classifiers. HIGHLIGHTS The use of Raman spectroscopy in conjunction with deep learning models and multivariate data analysis to diagnose and categorize blood plasma samples as cancerous or noncancerous and staging of breast cancer based on their chemical composition To address the issue of underfitting and overfitting caused by insufficient Raman spectral data, spectral data augmentation techniques were implemented The potential for this technique is used to accurately classify breast cancerous samples and hence reduce the number of unnecessary excisional breast biopsies Stage 3 and stage 2 of breast cancer were found to be structurally identical but can be distinguished from each other using PCA-Factorial Discriminant Analysis with high specificity and sensitivity for all BC stages The stacked OGRU-LSTM model outperformed other baseline classifiers for breast cancer detection and better differentiated various stages of breast cancer by employing multivariate data analysis technique GRAPHICAL ABSTRACT

A Multi-level Random Forest Model-Based Intrusion Detection Using Fuzzy Inference System for Internet of Things Networks

Intrusion detection (ID) methods are security frameworks designed to safeguard network information systems. The strength of an intrusion detection method is dependent on the robustness of the feature selection method. This study developed a multi-level random forest algorithm for intrusion detection using a fuzzy inference system. The strengths of the filter and wrapper approaches are combined in this work to create a more advanced multi-level feature selection technique, which strengthens network security. The first stage of the multi-level feature selection is the filter method using a correlation-based feature selection to select essential features based on the multi-collinearity in the data. The correlation-based feature selection used a genetic search method to choose the best features from the feature set. The genetic search algorithm assesses the merits of each attribute, which then delivers the characteristics with the highest fitness values for selection. A rule assessment has also been used to determine whether two feature subsets have the same fitness value, which ultimately returns the feature subset with the fewest features. The second stage is a wrapper method based on the sequential forward selection method to further select top features based on the accuracy of the baseline classifier. The selected top features serve as input into the random forest algorithm for detecting intrusions. Finally, fuzzy logic was used to classify intrusions as either normal, low, medium, or high to reduce misclassification. When the developed intrusion method was compared to other existing models using the same dataset, the results revealed a higher accuracy, precision, sensitivity, specificity, and F1-score of 99.46%, 99.46%, 99.46%, 93.86%, and 99.46%, respectively. The classification of attacks using the fuzzy inference system also indicates that the developed method can correctly classify attacks with reduced misclassification. The use of a multi-level feature selection method to leverage the advantages of filter and wrapper feature selection methods and fuzzy logic for intrusion classification makes this study unique.

Contextually Enriched Meta-Learning Ensemble Model for Urdu Sentiment Analysis

The task of analyzing sentiment has been extensively researched for a variety of languages. However, due to a dearth of readily available Natural Language Processing methods, Urdu sentiment analysis still necessitates additional study by academics. When it comes to text processing, Urdu has a lot to offer because of its rich morphological structure. The most difficult aspect is determining the optimal classifier. Several studies have incorporated ensemble learning into their methodology to boost performance by decreasing error rates and preventing overfitting. However, the baseline classifiers and the fusion procedure limit the performance of the ensemble approaches. This research made several contributions to incorporate the symmetries concept into the deep learning model and architecture: firstly, it presents a new meta-learning ensemble method for fusing basic machine learning and deep learning models utilizing two tiers of meta-classifiers for Urdu. The proposed ensemble technique combines the predictions of both the inter- and intra-committee classifiers on two separate levels. Secondly, a comparison is made between the performance of various committees of deep baseline classifiers and the performance of the suggested ensemble Model. Finally, the study’s findings are expanded upon by contrasting the proposed ensemble approach efficiency with that of other, more advanced ensemble techniques. Additionally, the proposed model reduces complexity, and overfitting in the training process. The results show that the classification accuracy of the baseline deep models is greatly enhanced by the proposed MLE approach.

An adaptive learning approach for customer churn prediction in the telecommunication industry using evolutionary computation and Naïve Bayes

Customer churn is a complex challenge for burgeoning competitive organizations, especially in telecommunication. It refers to customers that swiftly leave a company for a competitor. Acquiring new customers has cost the telecommunication industry more than keeping existing customers. Traditionally, customer churn prediction (CCP) models are applied to aid in analyzing customer behavior and achieving prediction accuracy, which allows the telecommunication industry to target prior retention efforts toward them. However, only accurate CCP based on the available data or already trained supervised model is inadequate for efficient churn prediction, as existing approaches have not been shown or designed to learn with the skill of adaptation to respond quickly to changes in the customer behavior or a decision. Therefore, it is essential to design an approach that easily adapts to learn from new decision scenarios and provides instant insights. This study proposes an adaptive learning approach for this perplexing problem of CCP using the Naïve Bayes classifier with a Genetic Algorithm (subclass of an Evolutionary Algorithm) based feature weighting approach. Further, the performance of the proposed approach is evaluated on publicly available datasets (i.e., BigML Telco churn, IBM Telco, and Cell2Cell) which significantly enhances the prediction performance as compared to the baseline classifier (i.e., Naïve Bayes with default setting, Deep-BP-ANN, CNN, Neural Network, Linear Regression, XGBoost, KNN, Logit Boost, SVM, and PCALB methods) in terms of average precision of 0.97, 0.97, 0.98, a recall rate that stands at 0.84, 0.94, 0.97, and F1-score of 0.89, 0.96, 0.97, an MCC of 0.89, 0.96, 0.97, and accuracy 0.95, 0.97, 0.98 on subject datasets, respectively.

Effect of Photobiomodulation With Different Wavelengths on Radiodermatitis Treatment.

Twenty rats were submitted to one radiotherapy session. After 15 days, the rats that developed radiodermatitis were divided into control groups, LED-630 nm, LED-850 nm, and LED-630 + 850 nm. The treatment regimen was one session lasting 10 minutes on alternate days for 21 days. We analyzed macroscopy aspects (RTOG scale), vascular density, dermal appendages, VEGF-a, TNF-alpha, MMP-9, and MMP-9 genic expression level. All LED groups revealed a two-point reduction on the radiodermatitis severity grade compared with the baseline classification. Dermal appendage and vascular analysis showed a higher counting in all LED groups compared to control. This study showed dermal appendages twice in the 630/850 nm group compared with the control group. The 630/850 nm group showed six times more arterioles than the control group. Regarding genic expression, this study showed a 10-fold decrease between LED-630 nm versus LED-630 + 850 nm (P = 0.02) interleukin-10 expression and a 12-fold decrease between control versus LED-630 nm (P = 0.006) and LED-850 nm (P = 0.002) in TNF-alpha. LED (630 nm, 850 nm, and 630 nm + 850 nm) showed benefit in the treatment of radiodermatitis, and the association of the 630 nm + 850 nm and 630 nm parameters demonstrated the best macroscopic and microscopic results.

A systematic review and meta-analysis of factors influencing ImPACT concussion testing in high school and collegiate athletes with self-reported ADHD and/or LD.

Sport concussion is a common injury, and athletes with attention-deficit/hyperactivity disorder (ADHD) and/or learning disorder (LD) are at increased risk and require specialized attention in clinical settings. Although systematic reviews of the relationship between ADHD/LD and concussion are reported in the literature, these reviews do not include quantitative syntheses. Additionally, no reviews have focused on the most commonly utilized concussion assessment, Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT). The current review provides an update of sport concussion assessment in athletes with ADHD and/or LD from 2000 to 2021 on these topics: baseline and postconcussion performance on ImPACT, baseline and postconcussion symptom reporting using the Postconcussion Symptom Scale, invalid baseline classification on ImPACT, and self-reported history of concussion. Meta-analyses were conducted on baseline ImPACT performance, symptom reporting, invalid baseline classification, and concussion rates. Thirty-four studies were included in systematic review and 19 were included in meta-analyses. Decreased baseline performance was found for athletes with ADHD (trivial to small effects), LD (small-to-medium effects), and ADHD/LD (small-to-medium effects). Increased baseline symptom reporting was found for athletes with ADHD (small effect). Increased odds of invalid baseline performance (trivial effect) and self-reported concussion history (small effect) were found in ADHD. These results provide the first quantitative synthesis of the literature in this area. It is recommended that future research further examines these topics in athletes with LD and co-occurring ADHD/LD (given the focus on ADHD), as well as the effects that all of these conditions may have on concussion recovery and return-to-play decision-making. (PsycInfo Database Record (c) 2023 APA, all rights reserved).