Jamming Efficacy Analysis of Chaff using AI/ML

Recently, a high dimensional classification framework has been proposed to introduce spatial and anatomical priors in support vector machine (SVM) optimization scheme for brain image analysis. However, classical SVM has to convert 3D discrete brain images naturally represented by higher-order tensors to one-dimensional vectors in order to meet the input requirements. This traditional method destroys the natural structure and correlation in the original data, and generates high dimensional vectors. In this manuscript, the method is improved by a modified support tensor machine (STM) algorithm to make full use of spatial prior and the inherent information of tensor. The new approach reduces memory requirements and computational complexity significantly, and it is comparably demonstrated by experimental results on classification of Alzheimer patients and elderly controls. Introduction Alzheimer disease (AD) is the most prevalent neurodegenerative dementia in worldwide, and thus prevention and early accurate diagnosis of AD is increasingly crucial. In the last few years, support vector machine (SVM) methods for AD subject classification have become an incredibly active research topic. In the related work, SVM approach, as same as feature selection and extraction, has been taking the specificity of neuroimaging data into account. As an example, a frame work is proposed in [1] to include spatial and anatomical priors into SVM by using regularization operators, and it indicates a flexible way to model various types of proximity. However, for the purpose of satisfying the input requirements of classical SVM, 3D discrete brain images naturally represented by higher-order tensors have to convert to vectors in advance. This conversion breaks the inherent structure and correlation in the original data, and easily produces high dimensional vectors and leads to a poor performance in time complexity and space complexity. In order to improve above method including spatial prior of 3D discrete brain images in SVM, a new method is proposed in this manuscript to maintain the natural structure in the original tensor data and include spatial prior of all frontal slices of the tensors in STM [2]. To illustrate the improvement, we apply the proposed method to classification of 299 subjects, 137 AD patients and 162 cognitively normal (CN) controls, as the same study population in [3]. It can be observed that the proposed method reduces size of memory requested and the time complexity significantly and produces better classification performances. Spatial prior in SVM SVM is a supervised learning algorithm introduced by Vapnik and performs classification by mapping the data into higher dimensional space [4]. Let (xs, ys), s∈1,...,S be a training set of instance-labeled pairs, xs∈Ω, ys∈{-1,1} and Ω is the input space, then SVM optimization problem can be written as:

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.

The Support Vector Machine (SVM) (Cortes and Vapnik, 1995; Vapnik, 1995; Burges, 1998) is intended to generate an optimal separating hyperplane by minimizing the generalization error without the assumption of class probabilities such as Bayesian classifier. The decision hyperplane of SVM is determined by the most informative data instances, called Support Vectors (SVs). In practice, these SVMs are a subset of the entire training data. By now, SVMs have been successfully applied in many applications, such as face detection, handwritten digit recognition, text classification, and data mining. Osuna et al. (1997) applied SVMs for face detection. Heisele et al. (2004) achieved high face detection rate by using 2nd degree SVM. They applied hierarchical classification and feature reduction methods to speed up face detection using SVMs. Feature extraction and reduction are two primary issues in feature selection that is essential in pattern classification. Whether it is for storage, searching, or classification, the way the data are represented can significantly influence performances. Feature extraction is a process of extracting more effective representation of objects from raw data to achieve high classification rates. For image data, many kinds of features have been used, such as raw pixel values, Principle Component Analysis (PCA), Independent Component Analysis (ICA), wavelet features, Gabor features, and gradient values. Feature reduction is a process of selecting a subset of features with preservation or improvement of classification rates. In general, it intends to speed up the classification process by keeping the most important class-relevant features.

Objective: Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions. Methods: Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from the Cancer Hospital of the Chinese Academy of Medical Sciences, Tianjin Central Hospital of Gynecology Obstetrics, Xinmi Maternal and Child Health Hospital of Henan Province, West China Second Affiliated Hospital of Sichuan University, and Heping Hospital Affiliated to Changzhi Medical College collected during April 2014 and March 2015. The hypermethylated gene fragments related to cervical cancer were selected by high-density, high-association, and hypermethylated gene fragment screening and the LASSO regression algorithm. Taking cervical intraepithelial neoplasia grade 2 (CIN2) or more severe lesions as the research outcome, machine learning predictive models based on the random forest (RF), naive Bayes (NB), and support vector machine (SVM) algorithm, respectively, were constructed. A total of 144 outpatient specimens were used as the training set and 80 cervical exfoliated cell specimens from women participating in the cervical cancer screening program were used as the test set to verify the predictive models. Using histological diagnosis results as the gold standard, the detection efficacy for CIN2 or more severe lesions of the three machine learning predictive models were compared with that of the human papilloma virus (HPV) detection and cytological diagnosis. Results: In the training set of 144 cases, there were 34 cases of HPV positivity, with a positive rate of 23.61%. Cytologically, there were 37 cases diagnosed as no intraepithelial lesion or malignancy (NILM), and 107 cases diagnosed as atypical squamous cells of undetermined significance (ASC-US) or above. Histologically, there were 28 cases without cervical intraepithelial neoplasia or benign cervical lesions, 31 cases of CIN1, 18 cases of CIN2, 31 cases of CIN3, and 36 cases of squamous cell carcinoma. Seven hypermethylated gene fragments were selected from 45 genes, and three machine learning prediction models based on the RF, NB, and SVM algorithm, respectively, were constructed. In the validation set of 80 cases, there were 28 cases of HPV positivity, with a positive rate of 35.00%. Cytologically, there were 65 cases diagnosed as NILM and 15 cases as ASC-US or above. Histologically, there were 39 cases without cervical intraepithelial neoplasia or benign cervical lesions, 10 cases of CIN1, 10 cases of CIN2, 11 cases of CIN3, and 10 cases of squamous cell carcinoma. In the validation set, the area under the curve (AUC) values of the RF model, NB model, SVM model, HPV detection, and cytological diagnosis of CIN2 or above were 0.90, 0.88, 0.82, 0.68, and 0.45, respectively. The DeLong test showed that there was no statistically significant difference in the AUC values between the RF, NB, and SVM models (all P＞0.05), and the AUC values of the RF and NB models were higher than that of HPV detection (both P＜0.01), and the AUC values of the RF, NB, and SVM models were higher than that of cytological diagnosis (all P＜0.01). Compared with the NB model, the sensitivity of the RF model was similar (80.65% vs. 77.42%), but the specificity of the NB model was much higher than that of the RF model (93.88% vs. 73.47%). Conclusion: Among the machine learning prediction models for cervical cancer and precancerous lesions constructed based on human DNA methylation, the NB model has good predictive performance for CIN2 and above lesions, and may be used for screening of cervical cancer and precancerous lesions.

The anterior cruciate ligament (ACL) plays an important role in stabilizing translation and rotation of the tibia relative to the femur. Individuals with ACL deficiency usually demonstrate alterations in gait characteristics. Evidence indicates that walking speed, alterations in kinetics and kinematics on the ACL deficient limb, and inter-limb asymmetries between deficient and intact knees may contribute to poor long-term outcomes following ACL deficiency. They corrode function of the knee joint and put it at higher risk of degeneration. For the purpose of developing an automatic and highly accurate system for detection of ACL deficiency, this study investigated the classification capability of different dynamical features extracted from gait kinematic and kinetic signals when evaluating their impact on different classification models. A general feature extraction framework was proposed and various dynamical features, such as recurrence rate, determinism and entropy from the recurrence quantification analysis, fuzzy entropy, Teager-Kaiser energy feature and statistical analysis, were included. Different classification models, including support vector machine (SVM), K-nearest neighbor (KNN), naive Bayes (NB) classifier, decision tree (DT) classifier and ensemble learning based Adaboost (ELA) classifier, derived for discriminant analysis of multiple dynamical gait features were evaluated for a comparative study. The effectiveness of this strategy was verified using a dataset of knee, hip and ankle kinematic and kinetic waveforms from 43 patients with unilateral ACL deficiency. When evaluated with 2-fold, 10-fold and leave-one-out cross-validation styles, the highest classification accuracy for discriminating between groups of ACL deficient and contralateral ACL intact knees was reported to be 91.22 %, 95.12% and 96.34%, respectively,by using the SVM classifier and the optimal feature set. For other four classifiers, KNN achieved the accuracy of 78.05%, 85.37% and 87.80%, respectively. NB achieved the accuracy of 57.56%, 60.98% and 61.22%, respectively. DT achieved the accuracy of 77.56%, 80.49% and 83.66%, respectively. ELA achieved the accuracy of 73.66%, 78.05% and 79.27%, respectively. Compared with other state-of-the-art methods, the results demonstrate superior performance and support the validity of the proposed method.

Software project development has high failure rate. Software project risk management may gain a high rate of return in investment. Establishing an intelligent risk evaluation model for project will be valuable in the analysis and control of project risks. In this paper, we employed neural network (NN) and support vector machine (SVM) approaches to establish a model for risk evaluation in project development. In the model, the input is a vector of software risk factors that were obtained through interview with 30 experts, and the output is the final outcome of the project. The data for modeling were collected from 120 real software projects through questionnaires. The experiment shows the model is valid. Interestingly, SVM is a powerful supervised learning method, and some believe that it is a more promising classification method that may someday supercede NN. In our study, the standard neural network model had lower prediction accuracy compared to SVM due to its tendency in finding local optima. However, after attempt in optimizing the neural network model with genetic algorithm, the experimental results showed that our enhanced model surpassed SVM in performance.

The rapid dissemination of information related to the K-Pop world, facilitated by social media, has made it easier to follow developments and controversies. One notable case that sparked extensive discussion on Twitter was the bullying allegations against Kim Garam of LE SSERAFIM. Researchers, using Twitter data, sought to analyze Indonesian public sentiment regarding this case through sentiment analysis, which classifies opinions as positive or negative. For processing textual data, text mining methods, particularly classification techniques, are employed. Two popular algorithms in text mining are the Naive Bayes classifier and the support vector machine (SVM). The Naive Bayes classifier is favored for its speed, simplicity, and high accuracy, while the SVM excels at identifying a hyperplane that maximizes the margin between classes. In this study, sentiment classification results were labeled as either positive or negative. The comparison between the Naive Bayes classifier and SVM for classifying responses to Kim Garam's bullying case on Twitter showed high accuracy rates: 93% for Naive Bayes and 97% for SVM. The higher accuracy of the SVM algorithm indicates its superiority over the Naive Bayes classifier in this context.

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

Single-cell RNA-seq overcomes the shortcomings of conventional transcriptome sequencing technology and could provide a powerful tool for distinguishing the transcriptome characteristics of various cell types in biological tissues, and comprehensively revealing the heterogeneity of gene expression between cells. Many Intelligent Computing methods have been presented to infer gene regulatory network (GRN) with single-cell RNA-seq data. In this paper, we investigate the performances of seven classifiers including support vector machine (SVM), random forest (RF), Naive Bayesian (NB), GBDT, logical regression (LR), decision tree (DT) and K-Nearest Neighbor (KNN) for solving the binary classification problems of GRN inference with single-cell RNA-seq data (Single_cell_GRN). In SVM, three different kernel functions (linear, polynomial and radial basis function) are utilized, respectively. Three real single-cell RNA-seq datasets from mouse and human are utilized. The experiment results prove that in most cases supervised learning methods (SVM, RF, NB, GBDT, LR, DT and KNN) perform better than unsupervised learning method (GENIE3) in terms of AUC. SVM, RF and KNN have the better performances than other four classifiers. In SVM, linear and polynomial kernels are more fit to model single-cell RNA-seq data.

The paper explores the potential of Support Vector Machines (SVM) approach in predicting 28-day compressive strength and slump flow of self-compacting concrete. Total of 80 data collected from the exiting literature were used in present work. To compare the performance of the technique, prediction was also done using a back propagation neural network model. For this data-set, RBF kernel worked well in comparison to polynomial kernel based support vector machines and provide a root mean square error of 4.688 (MPa) (correlation coefficient=0.942) for 28-day compressive strength prediction and a root mean square error of 7.825 cm (correlation coefficient=0.931) for slump flow. Results obtained for RMSE and correlation coefficient suggested a comparable performance by Support Vector Machine approach to neural network approach for both 28-day compressive strength and slump flow prediction.

Face recognition system, as any recognition process, depends highly on features extracted from face images. The selected features play a great role in deciding the recognition rate result. In this paper, a two-phase feature extraction and selection process is used for face recognition system. The process depends on histogram of Oriented Gradients (HOG) feature extraction and window size use to determine similarity between classes. Low number of features are used (big window size) to divide classes into small closed-similarity groups as first recognition phase. Then, the best matched class is found using larger number of features where differences between classes are bigger. The proposed method was applied to Essex face dataset using support vector machine (SVM) and Naïve Bayesian (NB) methods for comparison. The proposed method achieved 5% and 10% better recognition rate compared to SVM and NB respectively.

Credit risk analysis forms an important criterion for the endurance of the banking sector. Understanding credit liability of customer enhances the security stability of the banker. This research is aimed at comparing the predictive accuracy of data mining techniques with feature selection and extraction techniques in the default of credit card clients and suggesting the most efficient technique for analyzing credit card default. Customer's default payments in Taiwan were used as the dataset. Machine Learning classification algorithms like K-nearest neighbor(KNN), Naive Bayesian(NB), Decision Tree(DT) and Support Vector Machines(SVM) were applied to the dataset. Feature filter methods like information gain (IG), gain ratio (GR) and chi-square (CS) and Wrapper based methods like Best First search(BFS) and Hill Climbing search (HCS) were used to identify minimum features required. Feature extraction methods such as Principal Component Analysis(PCA) and Linear Discriminant Anal-ysis(LDA) were also applied. The minimum features of feature selection algorithms along with PCA and LDA were then applied to Base classifiers and their accuracies were tabulated and compared. SVM with feature selection and extraction techniques had the best predictive accuracy.

SMS spam detection is a crucial task in text classification, as unsolicited messages continue to pose security risks and inconvenience to users. This study explores the effectiveness of machine learning-based algorithms, particularly the Naive Bayes classifier, in accurately identifying and filtering spam messages. The primary objective is to classify SMS messages into spam or ham categories by analysing the occurrence of words and patterns within the text. The proposed approach involves a comprehensive pre-processing stage, including tokenization, stop-word removal, stemming, and feature extraction using techniques such as Term Frequency-Inverse Document Frequency (TF-IDF). The Naive Bayes algorithm is then trained on a labelled dataset to learn probabilistic distributions of words in spam and ham messages. Additionally, we compare the performance of Naive Bayes with other machine learning models like Support Vector Machines (SVM), Decision Trees, and Random Forest to assess their efficiency in spam detection. The experimental analysis demonstrates that the Naive Bayes classifier, due to its probabilistic nature, achieves high accuracy with minimal computational complexity. The study also evaluates precision, recall, F1-score, and overall classification accuracy to determine the best-performing algorithm. The results suggest that machine learning-based approaches significantly enhance SMS spam detection, reducing false positives and improving message filtering. Future work aims to integrate deep learning techniques and real-time detection mechanisms to further enhance accuracy and adaptability in dynamic environments.

These days, the Internet is developing at an exponential rate and can cover just about any data required. Nonetheless, the immense measure of web pages makes it more difficult to effectively discover the target data by a user. Therefore, an efficient method, for classifying this huge amount of data is essential if the web pages are to be exploited to its full potential. In the domain of automatic web page classifier many approaches have been tried to solve this problem using different Machine learning-based algorithms including Support Vector Machine (SVM), Naive Bayes, Decision Tree, K-Nearest Neighbor (K-NN) and Neural Networks. However, there is a lack of comparison between these algorithms to find a better framework for the classification and analysis of health related web pages. In this research study, we compare two commonly used supervised Machine Learning algorithms; Support Vector Machines (SVM) and Naive Bayes to classify web pages which provide drugs related information of patients for example side effects, patient action and follow-up information for patients. We use Unified Medical Language System (UMLS) to annotate the health related concepts in Web pages and train SVM and Naive Bayes classifiers in General Architecture for Text Engineering to classify health related and non-health related Web pages. The evaluation was performed using K-fold cross validation using four runs on a data set of fifty Web pages. Results found that SVM performed better to classify health and non-health related pages in terms of precision, recall and F-measure.

Background Malaria remains a major cause of illness and death among children under five in Nigeria, despite efforts to control transmission. Accurate and reliable prediction of malaria outbreaks is crucial for health authorities to take timely measures. This study aims to identify the most robust machine learning classification algorithms for predicting the status of malaria in children under five (0-59 months). Methods The 2021 Nigeria Malaria Indicator Survey (NMIS) included 10,655 children under five who were tested for malaria using the Rapid Diagnostic Test (RDT). Various machine learning models were explored, including Decision Trees, K-Nearest Neighbor, Naïve Bayes, Random Forest, Support Vector Machines, and Survey Logistic Regression, and their performance was evaluated through metrics such as accuracy, AUC, balanced accuracy, F1-Score, negative predictive value, precision, sensitivity, and specificity. Results Random Forest (RF) is the most robust and balanced classification model due to its superior accuracy (79%), precision (77%), recall (62%), F1-score (69%), and AUC (80%). Support Vector Machine (SVM) also demonstrated strong performance, particularly in accuracy (74%) and AUC (80%). Survey Logistic Regression (SLR) and Decision Tree (DT) offered moderate results but fell short compared to RF and SVM, indicating the need for further optimization. Naive Bayes (NB) and K-Nearest Neighbors (KNN) had limitations, making them less reliable for this task. Conclusion In conclusion, the study reveals that RF and SVM are the best classification models for predicting malaria status in children under five years old. RF is reliable and balanced, while SVM is preferred for recall. SLR and DT show potential but require optimization. NB and KNN have significant performance gaps, making them less suitable. These findings will help policymakers and malaria intervention programs address key factors, enabling more targeted public health interventions to reduce the malaria burden on young children and improve the well-being of vulnerable populations in Nigeria.