Code Injection Attacks Detection in Hybrid Applications Using CNN

The problem of classifying the sentiment analysis was found that there were many features for the sentiments that caused the less accuracy in classifying the sentiment, for example the negative class for those sentiments. The purpose of this study was to Figure out the amount of suitable features of each class for learning data with applied integration of information gain (IG) technique which used to reduce the factor and integrated to synthetic minority over-sampling technique (SMOTE) in order to adjust the imbalanced class. In this study, it enhanced the efficiency of accuracy in every class, and then it was evaluated by four methods consisting of J48, Naive Bayes, k-Nearest Neighbor where k=1, 2, 3, and Support Vector Machine (SVM) to compare the efficiency of accuracy. The TP Rate was employed as the evaluation metric for the accuracy of each class including the positive and the negative whereas the efficiency of accuracy was the TP Rate of Positive and the TP Rate of Negative. As the results of this study, it revealed that the IG and the SMOTE suggested the number of suitable features for the sentiment analysis. SVM method given the higher efficiency of the accuracy, that obtained the TP Rate of Positive as 86.50 % and TP Rate of Negative as 89.10 % and the level of SMOTE suitable by 300 %.

Background: Osteoporosis is a systemic skeletal disorder marked by reduced bone density and microarchitectural deterioration, leading to increased fracture risk. While the dual-energy X-ray absorptiometry (DEXA) scan is the World Health Organization (WHO)-recommended diagnostic standard, its limitations necessitate alternative methods. Emerging magnetic resonance imaging (MRI) techniques, radiomics, and machine learning promise to enhance osteoporosis diagnosis through detailed analysis of lumbar MRI apparent diffusion coefficient (ADC) maps, potentially revolutionizing early detection and treatment strategies. Objectives: In this study, we are going to evaluate the performance of machine learning (ML) models using radiomics features of lumbar MRI ADC map for osteoporosis detection, and to identify significant features and their diagnostic thresholds. Specific performance metrics such as accuracy, sensitivity, specificity, and Area Under the receiver operating characteristic (ROC) Curve (AUC) were assessed. Patients and Methods: This retrospective study employed a cross-sectional design, with a total of 140 cases, including 21 with osteoporosis. The study's inclusion criteria consisted of concurrent lumbar MRI and DEXA within a year, while exclusion criteria included infectious or neoplastic lumbar lesions, fractures, instrumentation, significant osteodegenerative changes, cases where the first four lumbar vertebrae were not included in the imaging field, and absence of diffusion-weighted imaging. Manual segmentation of lumbar vertebrae from ADC maps was performed to create a comprehensive dataset, comprising 5,580 radiomics features per case. Subsequently, the top five features selected by fast correlation-based filter (FCBF) were used to test the performance of seven Machine Learning algorithms (k-Nearest neighbors, decision tree, random forest, logistic regression, support vector machine, naive bayes, and neural network). Statistical tests and ROC curve analysis were conducted to determine the significance and thresholds of these features. Results: The study included 140 cases, with 132 females (94.3%) and 8 males (5.7%), and a mean age of 65.32 ± 8.50 years. The mean BMI was 31.43 ± 5.53 kg/m² for females and 26 ± 3.59 kg/m² for males. In terms of demographic differences, no significant age difference was found between the osteoporotic and non-osteoporotic groups (P = 0.889). However, the osteoporotic group had significantly lower mean body weight (64.90 ± 10.13 kg vs. 74.68 ± 13.94 kg, P = 0.003) and BMI (27.40 ± 4.38 kg/m² vs. 31.77 ± 5.52 kg/m², P = 0.001) compared to the non-osteoporotic group. The median interval between DEXA and lumbar MRI was 1 month (range 0.1 - 11.87 months). The Neural Network model demonstrated the highest performance with an AUC of 0.616 and a classification accuracy of 0.764 using all features. The Naive Bayes model, using the top five features selected by FCBF, showed the highest performance with an AUC of 0.913, accuracy of 0.907, sensitivity of 0.667, and specificity of 0.95. All ML models’ performance were elevated by feature selection. Independent t-tests and Mann-Whitney U tests identified 521 and 670 significant features, respectively (P < 0.05). ROC analysis revealed 58 features with AUC values above 0.70. Conclusion: This study's findings suggest that ML models, particularly the Naive Bayes algorithm, can effectively use lumbar ADC map radiomics to diagnose osteoporosis. These findings could enhance early detection and treatment strategies, potentially improving patient outcomes and reducing the burden of osteoporotic fractures. This study also established threshold values for significant features.

ObjectivesTo develop and identify machine learning (ML) models using pretreatment 2-deoxy-2-[18F]fluoro-D-glucose ([18F]-FDG)-positron emission tomography (PET)-based radiomic features to differentiate benign from malignant parotid gland diseases (PGDs).Materials and methodsThis retrospective study included 62 patients with 63 PGDs who underwent pretreatment [18F]-FDG-PET/computed tomography (CT). The lesions were assigned to the training (n = 44) and testing (n = 19) cohorts. In total, 49 [18F]-FDG-PET-based radiomic features were utilized to differentiate benign from malignant PGDs using five different conventional ML algorithmic models (random forest, neural network, k-nearest neighbors, logistic regression, and support vector machine) and the deep learning (DL)-based ensemble ML model. In the training cohort, each conventional ML model was constructed using the five most important features selected by the recursive feature elimination method with the tenfold cross-validation and synthetic minority oversampling technique. The DL-based ensemble ML model was constructed using the five most important features of the bagging and multilayer stacking methods. The area under the receiver operating characteristic curves (AUCs) and accuracies were used to compare predictive performances.ResultsIn total, 24 benign and 39 malignant PGDs were identified. Metabolic tumor volume and four GLSZM features (GLSZM_ZSE, GLSZM_SZE, GLSZM_GLNU, and GLSZM_ZSNU) were the five most important radiomic features. All five features except GLSZM_SZE were significantly higher in malignant PGDs than in benign ones (each p < 0.05). The DL-based ensemble ML model had the best performing classifier in the training and testing cohorts (AUC = 1.000, accuracy = 1.000 vs AUC = 0.976, accuracy = 0.947).ConclusionsThe DL-based ensemble ML model using [18F]-FDG-PET-based radiomic features can be useful for differentiating benign from malignant PGDs.Second abstractThe DL-based ensemble ML model using [18F]-FDG-PET-based radiomic features can overcome the previously reported limitation of [18F]-FDG-PET/CT scan for differentiating benign from malignant PGDs. The DL-based ensemble ML approach using [18F]-FDG-PET-based radiomic features can provide useful information for managing PGD.

Text mining is a special case of data mining which explore unstructured or semi-structured text documents, to establish valuable patterns and rules that indicate trends and significant features about specific topics. Text mining has been in pattern recognition, predictive studies, sentiment analysis and statistical theories in many areas of research, medicine, financial analysis, social life analysis, and business intelligence. Text mining uses concept of natural language processing and machine learning. Machine learning algorithms have been used and reported to give great results, but their performance of machine learning algorithms is affected by factors such as dataset domain, number of classes, length of the corpus, and feature selection techniques used. Redundant attribute affects the performance of the classification algorithm, but this can be reduced by using different feature selection techniques and dimensionality reduction techniques. Feature selection is a data preprocessing step that chooses a subset of input variable while eliminating features with little or no predictive information. Feature selection techniques are Information gain, Term Frequency, Term Frequency-Inverse document frequency, Mutual Information, and Chi-Square, which can use a filters, wrappers, or embedded approaches. To get the most value from machine learning, pairing the best algorithms with the right tools and processes is necessary. Little research has been done on the effect of feature selection techniques on classification accuracy for pairing of these algorithms with the best feature selection techniques for optimal results. In this research, a text classification experiment was conducted using incident management dataset, where incidents were classified into their resolver groups. Support vector machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB) and Decision tree (DT) machine learning algorithms were examined. Filtering approach was used on the feature selection techniques, with different ranking indices applied for optimal feature set and classification accuracy results analyzed. The classification accuracy results obtained using TF were, 88% for SVM, 70% for NB, 79% for Decision tree, and KNN had 55%, while Boolean registered 90%, 83%, 82% and 75%, for SVM, NB, DT, and KNN respectively. TF-IDF, had 91%, 83%, 76%, and 56% for SVM, NB, DT, and KNN respectively. The results showed that algorithm performance is affected by feature selection technique applied. SVM performed best, followed by DT, KNN and finally NB. In conclusion, presence of noisy data leads to poor learning performance and increases the computational time. The classifiers performed differently depending on the feature selection technique applied. For optimal results, the classifier that performed best together with the feature selection technique with the best feature subset should be applied for all types of data for accurate classification performance. Keywords: Text Classification, Supervised Machine Learning, Feature Selection DOI : 10.7176/JIEA/9-3-06 Publication date :May 31 st 2019

Sub-Saharan Africa continues to shoulder the heaviest burden of malaria. The 2024 World Health Organisation (WHO) malaria report highlighted that Africa contributed an alarming 94% of the global cases and 95% of the deaths. In the WHO African region, progress towards elimination and management of malaria is hindered by weak health systems and a lack of traditional diagnostic methods such as microscopy and malaria Rapid Diagnostic Tests (mRDTs). The primary aim of this paper is to develop a machine learning (ML) ensemble model for malaria diagnosis using clinical and demographic data, tailored for resource-limited settings. A retrospective study was conducted using 637 patient records from Gutu Mission Hospital and Gweru Provincial Hospital in Zimbabwe. Clinical symptoms (fever, chills, abdominal pain, headache and diarrhea) and demographic features (age, gender, residence and travel history) were analysed. Data preprocessing included handling class imbalance using the Synthetic Minority Oversampling Technique (SMOTE) and feature selection using Recursive Feature Elimination (RFE). Seven individual ML models, including Logistic regression (LR), Random Forest (RF), Decision Trees (DT), Gradient Boosting (GB), K-Nearest Neighbour (KNN), Naive Bayes (NB) and XGBoost were trained and evaluated on the malaria dataset. The individual models were further combined to build, train and evaluate ensemble models such as Bagging, Stacking, Soft Voting and AdaBoost. Model performance was assessed using accuracy, precision, confusion matrices, recall, F1 score, and AUC-ROC metrics. Clinical symptoms (chills: p-value=0.001, fever: p=0.003, diarrhea: p=0.01, abdominal pain: p&amp;lt; 0.001) were statistically significant predictors of malaria. Of the demographic factors, only travel history (p=0.02) showed a significant association with malaria. Among the seven individual ML models, GB achieved the highest predictive performance (Accuracy =0.94), followed by RF (Accuracy =0.94) and XGBoost (Accuracy =0.93). The stacking ensemble model outperformed all individual ML models and other ensemble models (bagging, soft voting and AdaBoost), achieving accuracy =0.96, precision =0.95, recall =0.98, F1 score =0.96 and AUC-ROC =0.98. This study demonstrates that ML, particularly ensemble models, can be used to improve malaria diagnosis significantly. The integration of these models into a web-based application could provide a scalable and accessible diagnostic tool for healthcare workers in resource-limited settings.

Machine learning based survival prediction of DLBCL: Using multimodal data.

Recent studies in scientific paper summarization have explored a new form of structured summary for a reference paper by grouping all cited and citing sentences together by facet. This involves three main tasks: (1) identifying cited text spans for citances (i.e., citing sentences), (2) classifying their discourse facets, and (3) generating a structured summary from the cited text spans and citances. This paper focuses on the first task, and approaches the task as binary classification to distinguish relevant pairs of citances and reference sentences from irrelevant pairs. We propose a new method that integrates feature selection and classification techniques to enhance classification performance. The proposed method investigates combinations of six feature selection methods (χ2-Statistics, Information Gain, Gain Ratio, Relief-F, Significance Attribute Evaluation, and Symmetrical Uncertainty), and five classification algorithms (k-Nearest Neighbors, Decision Tree, Support Vector Machine, Naïve Bayes, and Random Forest). Additionally, to address imbalanced data during training, we apply SMOTE (Synthetic Minority Over-sampling Technique) to introduce synthetic biases towards the minority. Experiments are conducted using the CL-SciSumm corpora to compare the effect of feature selection applied to classification. The results reveal the benefits of feature selection in significantly boosting performance of F1 score metric, and show that our method is competitive to the state-of-the-art methods in the CL-SciSumm evaluations.

The worldwide population and the healthcare industry have been put at risk by the coronavirus pandemic .Distinguishing COVID-19 affected individuals from healthy patient groups is an important job.Rapid and efficient screening methods are desperately needed in the battle against this unique illness in order to detect patients at an early stage and classify findings objectively.In order to identify COVID-19 from Chest X-ray (CXR) pictures, this research improved a robust Convolutional Neural Network (CNN) model that was constructed, called DeepCovNet, using the Synthetic Minority Over-sampling Technique (SMOTE).The principal issue tackled was the inherent class disparity in medical imaging datasets, wherein patients positive for COVID-19 are frequently underestimated in comparison to normal cases.The CNN model's performance was assessed after it was trained on a balanced dataset created via SMOTE.The improved method performs better than the current one with accuracy 96.0%, classifying COVID-19 patient cases using CXR images.The result of this study indicates a significant development in the healthcare industry and society at large in the battle against the pandemic employing cutting-edge deep learning techniques.This notable enhancement is related to the integration of (SMOTE).This study suggests that in order to improve model performance in scenarios with more severe class imbalance cases, additional research should investigate the combination of SMOTE with other data augmentation techniques, and this model should be extended to other medical imaging tasks, such as the detection of different lung diseases or pneumonias.

Drug-Protein Interaction (DPI) identification is crucial in drug discovery. The high dimensionality of drug and protein features poses challenges for accurate interaction prediction, necessitating the use of computational techniques. Docking-based methods rely on 3D structures, while ligand-based methods have limitations such as reliance on known ligands and neglecting protein structure. Therefore, the preferred approach is the chemogenomics-based approach using machine learning, which considers both drug and protein characteristics for DPI prediction. In machine learning, feature selection plays a vital role in improving model performance, reducing overfitting, enhancing interpretability, and making the learning process more efficient. It helps extract meaningful patterns from drug and protein data while eliminating irrelevant or redundant information, resulting in more effective machine-learning models. On the other hand, classification is of great importance as it enables pattern recognition, decision-making, predictive modeling, anomaly detection, data exploration, and automation. It empowers machines to make accurate predictions and facilitates efficient decision-making in DPI prediction. For this research work, protein data was sourced from the KEGG database, while drug data was obtained from the DrugBank data machine-learning base. To address the issue of imbalanced Drug Protein Pairs (DPP), different balancing techniques like Random Over Sampling (ROS), Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive SMOTE were employed. Given the large number of features associated with drugs and proteins, feature selection becomes necessary. Various feature selection methods were evaluated: Correlation, Information Gain (IG), Chi-Square (CS), and Relief. Multiple classification methods, including Support Vector Machines (SVM), Random Forest (RF), Adaboost, and Logistic Regression (LR), were used to predict DPI. Finally, this research identifies the best balancing, feature selection, and classification methods for accurate DPI prediction. This comprehensive approach aims to overcome the limitations of existing methods and provide more reliable and efficient predictions in drug-protein interaction studies.

Adolescents in higher education are more prone to Avoidant Personality Disorder (AVPD), which strongly affects academic achievement. The goal of this study was to create models for accurate prediction of the likelihood of Avoidant Personality Disorder among students in higher education. Information Gain, Gain Ratio, and Wrapper Approach are used as feature selection methods combined with data resampling techniques and machine learning, including Multi-Layer Perceptron Neural Network, Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machine. The findings revealed that the Wrapper approach gave higher accuracy than Information Gain and Gain Ratio approach. Further, using the Gain Ratio approach gives the model a slightly higher efficiency than the Information Gain. Furthermore, when comparing feature selection and data resampling, it was found that the model using feature selection had more higher model efficiency than data resampling alone. Additionally, combining the Synthetic Minority Over-sampling Technique with Edited Nearest Neighbor (SMOTE-ENN) considerably increased the model’s effectiveness. Finally, the model’s efficiency was at its maximum, with an accuracy of 95.52%, when the Wrapper approach was used in conjunction with the Synthetic Minority Over-sampling Technique, the Edited Nearest Neighbor algorithm, and the Multi-Layer Perceptron Neural Network.

HighlightsE-nose and computer vision combined with data fusion strategies were applied to trace tea origins.Pearson correlation analysis, IG, and F-scores were applied to modify the fusion strategies.The classification performances of different fusion strategies were compared.The strategies of IG_SVM_FL and IG_SVM_DS achieved the best results.Abstract. The traceability of tea origins is of great significance. In this study, an electronic nose (E-nose) and computer vision system (CVS) were jointly applied to acquire aroma and image signals of tea samples, aiming at identifying Longjing teas from different geographic origins including Jinyun (120° 7' E, 28° 65' N), Xihu (120° 13' E, 30° 27' N), Xinchang (120° 9' E, 29° 50' N), and Qian Daohu (119° 3' E, 29° 60' N). Data fusion was used to integrate the E-nose and CVS signals for comprehensively characterizing the tea samples. Four traditional fusion strategies including k-nearest neighbors (KNN) and support vector machine (SVM) based feature-level fusion (KNN_FL and SVM_FL) and Dempster-Shafer (D-S) evidence theory based decision-level strategies (KNN_DS and SVM_DS) were applied for classification modeling. Pearson analysis, information gain (IG), and F-scores were employed to modify the traditional fusion strategies to reduce inconsistent and redundant information in the fusion process. The results indicated that the original fusion strategies had no superiority over independent E-nose and CVS decision-making. With the feature selection methods, the modified fusion strategies generally exhibited better performance than the independent decision-making and original fusion strategies. Moreover, the IG-based fusion strategies, encompassing IG_SVM_FL and IG_SVM_DS, achieved the highest classification accuracy of 100%. Keywords: Computer vision, Electronic nose, Feature selection, Fusion strategies, Tea origins.

P68. Machine learning algorithms for predicting patient-controlled analgesia use after minimally invasive spine surgery

The classification of clinical notes into specific diagnostic categories is critical in healthcare, especially for mental health conditions like anxiety and adjustment disorder. In this study, we compare the performance of various artificial intelligence models, including both traditional machine learning approaches (random forest, support vector machine, K-nearest neighbors, decision tree, and eXtreme Gradient Boost) and deep learning models (DistilBERT and SciBERT), to classify clinical notes into these two diagnoses. Additionally, we implemented three oversampling strategies: No Oversampling, Random Oversampling, and Synthetic Minority Over-sampling Technique (SMOTE), to assess their impact on model performance. Hyperparameter tuning was also applied to optimize model accuracy. Our results indicate that oversampling techniques had minimal impact on model performance overall. The only exception was SMOTE, which showed a positive effect specifically with Bidirectional Encoder Representations from Transformers (BERT)-based models. However, hyperparameter optimization significantly improved accuracy across the models, enhancing their ability to generalize and perform on the dataset. The decision tree and eXtreme Gradient Boost models achieved the highest accuracy among machine learning approaches, both reaching 96%, while the DistilBERT and SciBERT models also attained 96% accuracy in the deep learning category. These findings underscore the importance of hyperparameter tuning in maximizing model performance. This study contributes to the ongoing research on AI-assisted diagnostic tools in mental health by providing insights into the efficacy of different model architectures and data balancing methods.

Prediction of the stage of cancer plays an important role in planning the course of treatment and has been largely reliant on imaging tools which do not capture molecular events that cause cancer progression. Gene-expression data-based analyses are able to identify these events, allowing RNA-sequence and microarray cancer data to be used for cancer analyses. Breast cancer is the most common cancer worldwide, and is classified into four stages - stages 1, 2, 3, and 4 [2]. While machine learning models have previously been explored to perform stage classification with limited success, multi-class stage classification has not had significant progress. There is a need for improved multi-class classification models, such as by investigating deep learning models. Gene-expression-based cancer data is characterised by the small size of available datasets, class imbalance, and high dimensionality. Class balancing methods must be applied to the dataset. Since all the genes are not necessary for stage prediction, retaining only the necessary genes can improve classification accuracy. The breast cancer samples are to be classified into 4 classes of stages 1 to 4. Invasive ductal carcinoma breast cancer samples are obtained from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets and combined. Two class balancing techniques are explored, synthetic minority oversampling technique (SMOTE) and SMOTE followed by random undersampling. A hybrid feature selection pipeline is proposed, with three pipelines explored involving combinations of filter and embedded feature selection methods: Pipeline 1 - minimum-redundancy maximum-relevancy (mRMR) and correlation feature selection (CFS), Pipeline 2 - mRMR, mutual information (MI) and CFS, and Pipeline 3 - mRMR and support vector machine-recursive feature elimination (SVM-RFE). The classification is done using deep learning models, namely deep neural network, convolutional neural network, recurrent neural network, a modified deep neural network, and an AutoKeras generated model. Classification performance post class-balancing and various feature selection techniques show marked improvement over classification prior to feature selection. The best multiclass classification was found to be by a deep neural network post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with a Cohen-Kappa score of 0.303 and a classification accuracy of 53.1%. For binary classification into early and late-stage cancer, the best performance is obtained by a modified deep neural network (DNN) post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with an accuracy of 81.0% and a Cohen-Kappa score (CKS) of 0.280. This pipeline also showed improved multiclass classification performance on neuroblastoma cancer data, with a best area under the receiver operating characteristic (auROC) curve score of 0.872, as compared to 0.71 obtained in previous work, an improvement of 22.81%. The results and analysis reveal that feature selection techniques play a vital role in gene-expression data-based classification, and the proposed hybrid feature selection pipeline improves classification performance. Multi-class classification is possible using deep learning models, though further improvement particularly in late-stage classification is necessary and should be explored further.

Background and aimsThe birth weight of a newborn is a crucial factor that affects their overall health and future well-being. Low birth weight (LBW) is a widespread global issue, which the World Health Organization defines as weighing less than 2,500 g. LBW can have severe negative consequences on an individual’s health, including neonatal mortality and various health concerns throughout their life. To address this problem, this study has been conducted using BDHS 2017–2018 data to uncover important aspects of LBW using a variety of machine learning (ML) approaches and to determine the best feature selection technique and best predictive ML model.MethodsTo pick out the key features, the Boruta algorithm and wrapper method were used. Logistic Regression (LR) used as traditional method and several machine learning classifiers were then used, including, DT (Decision Tree), SVM (Support Vector Machine), NB (Naïve Bayes), RF (Random Forest), XGBoost (eXtreme Gradient Boosting), and AdaBoost (Adaptive Boosting), to determine the best model for predicting LBW. The model’s performance was evaluated based on the specificity, sensitivity, accuracy, F1 score and AUC value.ResultsResult shows, Boruta algorithm identifies eleven significant features including respondent’s age, highest education level, educational attainment, wealth index, age at first birth, weight, height, BMI, age at first sexual intercourse, birth order number, and whether the child is a twin. Incorporating Boruta algorithm’s significant features, the performance of traditional LR and ML methods including DT, SVM, NB, RF, XGBoost, and AB were evaluated where LR, had a specificity, sensitivity, accuracy and F1 score of 0.85, 0.5, 85.15% and 0.915. While the ML methods DT, SVM, NB, RF, XGBoost, and AB model’s respective accuracy values were 85.35%, 85.15%, 84.54%, 81.18%, and 84.41%. Based on the specificity, sensitivity, accuracy, F1 score and AUC, RF (specificity = 0.99, sensitivity = 0.58, accuracy = 85.86%, F1 score = 0.9243, AUC = 0.549) outperformed the other methods. Both the classical (LR) and machine learning (ML) models’ performance has improved dramatically when important characteristics are extracted using the wrapper method. The LR method identified five significant features with a specificity, sensitivity, accuracy and F1 score of 0.87, 0.33, 87.12% and 0.9309. The region, whether the infant is a twin, and cesarean delivery were the three key features discovered by the DT and RF models, which were implemented using the wrapper technique. All three models had the identical F1 score of 0.9318. However, “child is twin” was recognized as a significant feature by the SVM, NB, and AB models, with an F1 score of 0.9315. Ultimately, with an F1 score of 0.9315, the XGBoost model recognized “child is twin” and “age at first sex” as relevant features. Random Forest again beat the other approaches in this instance.ConclusionsThe study reveals Wrapper method as the optimal feature selection technique. The ML method outperforms traditional methods, with Random Forest (RF) being the most effective predictive model for Low-Birth-Weight prediction. The study suggests that policymakers in Bangladesh can mitigate low birth weight newborns by considering identified risk factors.