Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

Fake news and its significance carried the significance of affecting diverse aspects of diverse entities, ranging from a city lifestyle to a country global relativity, various methods are available to collect and determine fake news. The recently developed machine learning (ML) models can be employed for the detection and classification of fake news. This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine (CAS-WELM) for Cybersecurity Fake News Detection and Classification. The goal of the CAS-WELM technique is to discriminate news into fake and real. The CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embedding process. Then, N-gram based feature extraction technique is derived to generate feature vectors. Lastly, WELM model is applied for the detection and classification of fake news, in which the weight value of the WELM model can be optimally adjusted by the use of CAS algorithm. The performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several dimensions. The experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.     

Machine Learning Based Cybersecurity Threat Detection for Secure IoT Assisted Cloud Environment

The Internet of Things (IoT) is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare, in health service to energy, and in developed to transport. Cybersecurity develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that solved. The progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence (AI) and Machine Learning (ML) devices are crucial fact to realize security in IoT platform. It can be required for minimizing the issues of security based on IoT devices efficiently. Thus, this research proposal establishes novel mayfly optimized with Regularized Extreme Learning Machine technique called as MFO-RELM model for Cybersecurity Threat classification and detection from the cloud and IoT environments. The proposed MFO-RELM model provides the effective detection of cybersecurity threat which occur in the cloud and IoT platforms. To accomplish this, the MFO-RELM technique pre-processed the actual cloud and IoT data as to meaningful format. Besides, the proposed models will receive the pre-processing data and carry out the classifier method. For boosting the efficiency of the proposed models, the MFO technique was utilized to it. The experiential outcome of the proposed technique was tested utilizing the standard CICIDS 2017 dataset, and the outcomes are examined under distinct aspects.     

Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

Natural Language Processing (NLP) for the Arabic language has gained much significance in recent years. The most commonly-utilized NLP task is the ‘Text Classification’ process. Its main intention is to apply the Machine Learning (ML) approaches for automatically classifying the textual files into one or more pre-defined categories. In ML approaches, the first and foremost crucial step is identifying an appropriate large dataset to test and train the method. One of the trending ML techniques, i.e., Deep Learning (DL) technique needs huge volumes of different types of datasets for training to yield the best outcomes. The current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machine-based Arabic Corpus Classification (DODHBM-ACC) model in this background. The presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding process. For Arabic text classification, the DHBM technique is utilized. This technique is a hybrid version of the Deep Boltzmann Machine (DBM) and Deep Belief Network (DBN). It has the advantage of learning the decisive intention of the classification process. To adjust the hyperparameters of the DHBM technique, the Dice Optimization Algorithm (DOA) is exploited in this study. The experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC model. The outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.     

Exploring Students Engagement Towards the Learning Management System (LMS) Using Learning Analytics

Learning analytics is a rapidly evolving research discipline that uses the insights generated from data analysis to support learners as well as optimize both the learning process and environment. This paper studied students’ engagement level of the Learning Management System (LMS) via a learning analytics tool, student’s approach in managing their studies and possible learning analytic methods to analyze student data. Moreover, extensive systematic literature review (SLR) was employed for the selection, sorting and exclusion of articles from diverse renowned sources. The findings show that most of the engagement in LMS are driven by educators. Additionally, we have discussed the factors in LMS, causes of low engagement and ways of increasing engagement factors via the Learning Analytics approach. Nevertheless, apart from recognizing the Learning Analytics approach as being a successful method and technique for analyzing the LMS data, this research further highlighted the possibility of merging the learning analytics technique with the LMS engagement in every institution as being a direction for future research.     

Applications of Machine Learning: Towards knowledge synthesis

This paper shows, by discussing a number of Machine Learning (ML) applications, that the existing ML techniques can be effectively applied in knowledge acquisition for expert systems, thereby alleviating the known knowledge acquisition bottleneck. Analysis in domains of practical interest indicates that the performance accuracy of knowledge induced through learning from examples compares very favourably with the accuracy of best human experts. Also, in addition to accuracy, there are encouraging examples regarding the clarity and meaningfulness of induced knowledge. This points towards automated knowledge synthesis, although much further research is needed in this direction. The state of the art of some approaches to Machine Learning is assessed relative to their practical applicability and the characteristics of a problem domain.     

Big Data Analytics with Optimal Deep Learning Model for Medical Image Classification

In recent years, huge volumes of healthcare data are getting generated in various forms. The advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and quicker. Due to such massive generation of big data, the utilization of new methods based on Big Data Analytics (BDA), Machine Learning (ML), and Artificial Intelligence (AI) have become essential. In this aspect, the current research work develops a new Big Data Analytics with Cat Swarm Optimization based deep Learning (BDA-CSODL) technique for medical image classification on Apache Spark environment. The aim of the proposed BDA-CSODL technique is to classify the medical images and diagnose the disease accurately. BDA-CSODL technique involves different stages of operations such as preprocessing, segmentation, feature extraction, and classification. In addition, BDA-CSODL technique also follows multi-level thresholding-based image segmentation approach for the detection of infected regions in medical image. Moreover, a deep convolutional neural network-based Inception v3 method is utilized in this study as feature extractor. Stochastic Gradient Descent (SGD) model is used for parameter tuning process. Furthermore, CSO with Long Short-Term Memory (CSO-LSTM) model is employed as a classification model to determine the appropriate class labels to it. Both SGD and CSO design approaches help in improving the overall image classification performance of the proposed BDA-CSODL technique. A wide range of simulations was conducted on benchmark medical image datasets and the comprehensive comparative results demonstrate the supremacy of the proposed BDA-CSODL technique under different measures.     

Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment

Recently, Internet of Things (IoT) devices produces massive quantity of data from distinct sources that get transmitted over public networks. Cybersecurity becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be resolved. The development of automated tools for cyber threat detection and classification using machine learning (ML) and artificial intelligence (AI) tools become essential to accomplish security in the IoT environment. It is needed to minimize security issues related to IoT gadgets effectively. Therefore, this article introduces a new Mayfly optimization (MFO) with regularized extreme learning machine (RELM) model, named MFO-RELM for Cybersecurity Threat Detection and classification in IoT environment. The presented MFO-RELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT environment. For accomplishing this, the MFO-RELM model pre-processes the actual IoT data into a meaningful format. In addition, the RELM model receives the pre-processed data and carries out the classification process. In order to boost the performance of the RELM model, the MFO algorithm has been employed to it. The performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects.     

Ensemble of Efficient Minimal Learning Machines for Classification and Regression

Minimal Learning Machine (MLM) is a recently proposed supervised learning algorithm with performance comparable to most state-of-the-art machine learning methods. In this work, we propose ensemble methods for classification and regression using MLMs. The goal of ensemble strategies is to produce more robust and accurate models when compared to a single classifier or regression model. Despite its successful application, MLM employs a computationally intensive optimization problem as part of its test procedure (out-of-sample data estimation). This becomes even more noticeable in the context of ensemble learning, where multiple models are used. Aiming to provide fast alternatives to the standard MLM, we also propose the Nearest Neighbor Minimal Learning Machine and the Cubic Equation Minimal Learning Machine to cope with classification and single-output regression problems, respectively. The experimental assessment conducted on real-world datasets reports that ensemble of fast MLMs perform comparably or superiorly to reference machine learning algorithms.     

IP流量分类算法中特征选择作用分析

基于流的特征并使用机器学习技术进行网络流量分类是目前网络流量分类的主流技术。由于许多流的特征可用于流分类,其中有许多是不相关和冗余的特征,因此特征选择对算法性能的优化具有重要的作用。将基于过滤的特征选择方法应用于C4.5、Bayesnet、NBD、NBK等分类算法,实验结果表明该方法在无损于分类准确性的同时能够改进计算性能。     

MLife: a lite framework for machine learning lifecycle initialization

Machine Learning - Machine learning (ML) lifecycle is a cyclic process to build an efficient ML system. Though a lot of commercial and community (non-commercial) frameworks have been proposed to...     

An Ensemble Machine Learning Technique for Stroke Prognosis

Stroke is a life-threatening disease usually due to blockage of blood or insufficient blood flow to the brain. It has a tremendous impact on every aspect of life since it is the leading global factor of disability and morbidity. Strokes can range from minor to severe (extensive). Thus, early stroke assessment and treatment can enhance survival rates. Manual prediction is extremely time and resource intensive. Automated prediction methods such as Modern Information and Communication Technologies (ICTs), particularly those in Machine Learning (ML) area, are crucial for the early diagnosis and prognosis of stroke. Therefore, this research proposed an ensemble voting model based on three Machine Learning (ML) algorithms: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM). We apply data preprocessing to manage the outliers and useless instances in the dataset. Furthermore, to address the problem of imbalanced data, we enhance the minority class’s representation using the Synthetic Minority Over-Sampling Technique (SMOTE), allowing it to engage in the learning process actively. Results reveal that the suggested model outperforms existing studies and other classifiers with 0.96% accuracy, 0.97% precision, 0.97% recall, and 0.96% F1-score. The experiment demonstrates that the proposed ensemble voting model outperforms state-of-the-art and other traditional approaches.     

#br# 耦合样本先验分布信息的加权极限学习机

极限学习机广泛用于分类、聚类、回归等任务中，但在处理类不平衡分类问题时，前人未充分考虑样本先验分布信息对分类性能的影响。针对此问题，本文提出耦合样本先验分布信息的加权极限学习机（Coupling sample Prior distribution Weighted Extreme Learning Machine，CPWELM）算法。该算法基于加权极限学习机，充分探讨不同分布样本点的重要程度，以此构造代价矩阵，进而提升分类器性能。本文通过12个不平衡数据集，对CPWELM算法的可行性及有效性进行了验证。结果表明，相比同类其他算法，CPWELM算法的性能更优。     

Polarimetric SAR image classification by Boosted Multiple-Kernel Extreme Learning Machines with polarimetric and spatial features

In this paper, we propose new approach: Boosted Multiple-Kernel Extreme Learning Machines (BMKELMs), a multiple kernel version of Kernel Extreme Learning Machine (KELM). We apply it to the classification of fully polarized SAR images using multiple polarimetric and spatial features. Compared with other conventional multiple kernel learning methods, BMKELMs exploit KELM with the boosting paradigm coming from ensemble learning (EL) to train multiple kernels. Additionally, different fusion strategies such as majority voting, weighted majority voting, MetaBoost, and ErrorPrune were used for selecting the classification result with the highest overall accuracy. To show the performance of BMKELMs against other state-of-the-art approaches, two L-band fully polarimetric airborne SAR images (Airborne Synthetic Aperture Radar (AIRSAR) data collected by NASA JPL over the Flevoland area of The Netherlands and Electromagnetics Institute Synthetic Aperture Radar (EMISAR) data collected by DLR over Foulum in Denmark) were considered. Experimental results indicate that the proposed technique achieves the highest classification accuracy values when dealing with multiple features, such as a combination of polarimetric coherency and multi-scale spatial features.     

An Efficient Unsupervised Learning Approach for Detecting Anomaly in Cloud

The Cloud system shows its growing functionalities in various industrial applications. The safety towards data transfer seems to be a threat where Network Intrusion Detection System (NIDS) is measured as an essential element to fulfill security. Recently, Machine Learning (ML) approaches have been used for the construction of intellectual IDS. Most IDS are based on ML techniques either as unsupervised or supervised. In supervised learning, NIDS is based on labeled data where it reduces the efficiency of the reduced model to identify attack patterns. Similarly, the unsupervised model fails to provide a satisfactory outcome. Hence, to boost the functionality of unsupervised learning, an effectual auto-encoder is applied for feature selection to select good features. Finally, the Naïve Bayes classifier is used for classification purposes. This approach exposes the finest generalization ability to train the data. The unlabelled data is also used for adoption towards data analysis. Here, redundant and noisy samples over the dataset are eliminated. To validate the robustness and efficiency of NIDS, the anticipated model is tested over the NSL-KDD dataset. The experimental outcomes demonstrate that the anticipated approach attains superior accuracy with 93%, which is higher compared to J48, AB tree, Random Forest (RF), Regression Tree (RT), Multi-Layer Perceptrons (MLP), Support Vector Machine (SVM), and Fuzzy. Similarly, False Alarm Rate (FAR) and True Positive Rate (TPR) of Naive Bayes (NB) is 0.3 and 0.99, respectively. When compared to prevailing techniques, the anticipated approach also delivers promising outcomes.     

Diabetic Retinopathy Diagnosis Using Interval Neutrosophic Segmentation with Deep Learning Model

In recent times, Internet of Things (IoT) and Deep Learning (DL) models have revolutionized the diagnostic procedures of Diabetic Retinopathy (DR) in its early stages that can save the patient from vision loss. At the same time, the recent advancements made in Machine Learning (ML) and DL models help in developing Computer Aided Diagnosis (CAD) models for DR recognition and grading. In this background, the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network (ODBN) model i.e., NS-ODBN model for diagnosis of DR. The presented model involves Interval Neutrosophic Set (INS) technique to distinguish the diseased areas in fundus image. In addition, three feature extraction techniques such as histogram features, texture features, and wavelet features are used in this study. Besides, Optimal Deep Belief Network (ODBN) model is utilized as a classification model for DR. ODBN model involves Shuffled Shepherd Optimization (SSO) algorithm to regulate the hyperparameters of DBN technique in an optimal manner. The utilization of SSO algorithm in DBN model helps in increasing the detection performance of the model significantly. The presented technique was experimentally evaluated using benchmark DR dataset and the results were validated under different evaluation metrics. The resultant values infer that the proposed INS-ODBN technique is a promising candidate than other existing techniques.     

最小类内方差支持向量引导的字典学习

支持向量引导的字典学习算法依据大间隔分类原则,仅考虑每类编码向量边界条件建立决策超平面,未利用数据的分布信息,在一定程度上限制了模型的泛化能力.为解决该问题,提出最小类内方差支持向量引导的字典学习算法.将融合Fisher线性鉴别分析和支持向量机大间隔分类准则的最小类内方差支持向量机作为鉴别条件,在模型分类器的交替优化过程中,充分考虑编码向量的分布信息,保障同类编码向量总体一致的同时降低向量间的耦合度并修正分类矢量,从而挖掘编码向量鉴别信息,使其更好地引导字典学习以提高算法分类性能.在人脸、物体和手写数字识别数据集上的实验结果表明,在大部分样本和原子数量条件下,该算法的识别率和原子鲁棒性均优于K奇异值分解、局部特征和类标嵌入约束等经典字典学习算法.     

基于改进花粉算法的极限学习机分类模型

针对多输出极限学习机（MELM）分类模型输入层权值和阈值随机选取导致的分类精度波动问题,提出一种基于改进花粉算法（CS-ACFPA）的极限学习机多分类模型（CS-ACFPA-MELM）。利用自适应算子和Tent策略优化花粉算法的寻优方式,构造一种基于代价敏感的适应度函数,使花粉算法能够更好地匹配MELM模型的输出,最后使用改进的花粉算法和基于代价敏感的适应度函数优化极限学习机的输入权值和阈值,以提高MELM模型的的分类性能。通过对比实验验证了CS-ACFPA算法对MELM模型改进的有效性,并且体现了CS-ACFPA-MELM模型在大规模样本上的优势以及小样本上的适用性。     

Defense strategies for Adversarial Machine Learning: A survey

Adversarial Machine Learning (AML) is a recently introduced technique, aiming to deceive Machine Learning (ML) models by providing falsified inputs to render those models ineffective. Consequently, most researchers focus on detecting new AML attacks that can undermine existing ML infrastructures, overlooking at the same time the significance of defense strategies. This article constitutes a survey of the existing literature on AML attacks and defenses with a special focus on a taxonomy of recent works on AML defense techniques for different application domains, such as audio, cyber-security, NLP, and computer vision. The proposed survey also explores the methodology of the defense solutions and compares them using several criteria, such as whether they are attack- and/or domain-agnostic, deploy appropriate AML evaluation metrics, and whether they share their source code and/or their evaluation datasets. To the best of our knowledge, this article constitutes the first survey that seeks to systematize the existing knowledge focusing solely on the defense solutions against AML and providing innovative directions for future research on tackling the increasing threat of AML.     

基于成员相似性的集成极端学习机

为了增大各成员间的差异度以改善集成系统的性能,提出了一种基于成员间相似性选择的集成极端学习机(ELM)。首先,筛选出分类性能较高的备选极端学习机;其次,根据成员间的相似性运用微粒群算法(PSO)进一步选出最优的集成成员集合。通过选出相似度低的极端学习机来提高集成成员间差异度,从而有效提高集成系统的分类能力。选出的成员学习机在不同的集成规则下都具有良好性能。在四个UCI数据集上的实验结果表明,与经典的集成极端学习机相比,基于成员相似性选择的集成极端学习机具有更优的泛化性能和稳定性。