Detecting Arabic Spam Reviews in Social Networks Based on Classification Algorithms

Reviews or comments that users leave on social media have great importance for companies and business entities. New product ideas can be evaluated based on customer reactions. However, this use of social media is complicated by those who post spam on social media in the form of reviews and comments. Designing methodologies to automatically detect and block social media spam is complicated by the fact that spammers continuously develop new ways to leave their spam comments. Researchers have proposed several methods to detect English spam reviews. However, few studies have been conducted to detect Arabic spam reviews. This article proposes a keyword-based method for detecting Arabic spam reviews. Keywords or Features are subsets of words from the original text that are labelled as important. A term's weight, Term Frequency–Inverse Document Frequency (TF-IDF) matrix, and filter methods (such as information gain, chi-squared, deviation, correlation, and uncertainty) have been used to extract keywords from Arabic text. The method proposed in this article detects Arabic spam in Facebook comments. The dataset consists of 3,000 Arabic comments extracted from Facebook pages. Four different machine learning algorithms are used in the detection process, including C4.5, kNN, SVM, and Naïve Bayes classifiers. The results show that the Decision Tree classifier outperforms the other classification algorithms, with a detection accuracy of 92.63%.

Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

Recently, many researchers have directed their attention to methods of predicting shorelines by the use of multispectral images. Thus, a simple and optimised method using image enhancements is proposed to improve the low contrast of the Satellite pour l'Observation de la Terre-5 (SPOT-5) images in the detection of shorelines. The near-infrared (NIR) channel is important in this study to ensure the contrast of the vegetated area and sea classification, due to the high reflectance of leaves in the near infrared wavelength region. This study used five scenes of interest to show the different results in shoreline detection. The results demonstrated that the proposed method performed in an enhanced manner as compared to current methods when dealing with the low contrast ratio of SPOT-5 images. As a result, by utilising the near-infrared histogram equalization (NIR-HE), the contrast of all datasets was efficiently restored, producing a higher efficiency in edge detection, and achieving higher overall accuracy. The improved filtering method showed significantly better shoreline detection results than the other filter methods. It was concluded that this method would be useful for detecting and monitoring the shoreline edge in Tanjung Piai.

The impact of the COVID-19 pandemic on the affective filter of EFL learners in an online classroom

Introduction: the integration of technology in language teaching, especially in the context of the COVID-19 pandemic caused the search for different pedagogical strategies. Due to this drastic change in education along with the rapid access to online resources have placed technology as the most important tool in teaching. Therefore, when planning a class, the teacher must consider digital tools and dynamic methodologies to streamline and motivate students to reduce their affective filter. Objective: the current research aims to determine the effectiveness of Kahoot in the reduction of the impact of the EFL students’ affective filter. Methods: this study developed applied research with a mixed method approach and descriptive scope, with a quasi-experimental design. 42 students actively participated. For the data gathering, two main tools of data collection were used, namely a survey to look for information about the impact of students’ affective filter during the virtual classes and a pre and post-test based on reading comprehension to check the students’ English performance. Conclusions: the students are facing negative experiences in their learning of English because their affective filter is high. Besides, after the intervention phase with the use of Kahoot, the students improved their English performance in the reading comprehension skill. Therefore, it is suggested to continue sampling innovative technological resources to enhance students’ performance.

Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques?

This study analyzed highly correlated, feature-rich datasets from hyperspectral remote sensing data using multiple statistical and machine-learning methods. The effect of filter-based feature selection methods on predictive performance was compared. In addition, the effect of multiple expert-based and data-driven feature sets, derived from the reflectance data, was investigated. Defoliation of trees (%), derived from in situ measurements from fall 2016, was modeled as a function of reflectance. Variable importance was assessed using permutation-based feature importance. Overall, the support vector machine (SVM) outperformed other algorithms, such as random forest (RF), extreme gradient boosting (XGBoost), and lasso (L1) and ridge (L2) regressions by at least three percentage points. The combination of certain feature sets showed small increases in predictive performance, while no substantial differences between individual feature sets were observed. For some combinations of learners and feature sets, filter methods achieved better predictive performances than using no feature selection. Ensemble filters did not have a substantial impact on performance. The most important features were located around the red edge. Additional features in the near-infrared region (800–1000 nm) were also essential to achieve the overall best performances. Filter methods have the potential to be helpful in high-dimensional situations and are able to improve the interpretation of feature effects in fitted models, which is an essential constraint in environmental modeling studies. Nevertheless, more training data and replication in similar benchmarking studies are needed to be able to generalize the results.

Methods for Oxygenation of Continuous Cultures of Brewer’s Yeast, Saccharomyces cerevisiae

Maintaining steady-state, aerobic cultures of yeast in a bioreactor depends on the configuration of the bioreactor system as well as the growth medium used. In this paper, we compare several conventional aeration methods with newer filter methods using a novel optical sensor array to monitor dissolved oxygen, pH, and biomass. With conventional methods, only a continuously stirred tank reactor configuration gave high aeration rates for cultures in yeast extract peptone dextrose (YPD) medium. For filters technologies, only a polydimethylsiloxan filter provided sufficient aeration of yeast cultures. Further, using the polydimethylsiloxan filter, the YPD medium gave inferior oxygenation rates of yeast compared to superior results with Synthetic Complete medium. It was found that the YPD medium itself, not the yeast cells, interfered with the filter giving the low oxygen transfer rates based on the volumetric transfer coefficient (KLa). The results are discussed for implications of miniaturized bioreactors in low-gravity environments.

A Class of Inexact Secant Algorithms with Line Search Filter Method for Nonlinear Programming

We propose a class of inexact secant methods in association with the line search filter technique for solving nonlinear equality constrained optimization. Compared with other filter methods that combine the line search method applied in most large-scale optimization problems, the inexact line search filter algorithm is more flexible and realizable. In this paper, we focus on the analysis of the local superlinear convergence rate of the algorithms, while their global convergence properties can be obtained by making an analogy with our previous work. These methods have been implemented in a Matlab code, and detailed numerical results indicate that the proposed algorithms are efficient for 43 problems from the CUTEr test set.

Rainfall Forecasting Model Using ARIMA and Kalman Filter in Makassar, Indonesia

Many forecasting methods have been used for forecasting rainfall data. Kalman Filter is one of the forecasting methods that could give better forecasts. To our knowledge, the Kalman Filter method has not been used to forecast rainfall data in Makassar, Indonesia. This study aims to provide more precise forecasts for rainfall data in Makassar, Indonesia by using Autoregressive Integrated Moving Average (ARIMA) and Kalman Filter methods. Rainfall data from January 2010 to December 2020 were used. The best model selection is based on the smallest Mean Absolute Percentage Error (MAPE) value. The results showed that the best ARIMA model is ARIMA(0,1,1)(0,1,1)12 with MAPE is 111.48, while MAPE value by using the Kalman Filter algorithm is 47.00 indicating that Kalman Filter has better prediction than ARIMA model.

Genetic Algorithm Ensemble Filter Methods on Kidney Disease Classification

Kidney failure will give effect to the human body, and it can lead to a series of seriously illness and even causing death. Machine learning plays important role in disease classification with high accuracy and shorter processing time as compared to clinical lab test. There are 24 attributes in the Chronic K idney Disease (CKD) clinical dataset, which is considered as too much of attributes. To improve the performance of the classification, filter feature selection methods used to reduce the dimensions of the feature and then the ensemble algorithm is used to identify the union features that selected from each filter feature selection. The filter feature selection that implemented in this research are Information Gain (IG), Chi-Squares, ReliefF and Fisher Score. Genetic Algorithm (GA) is used to select the best subset from the ensemble result of the filter feature selection. In this research, Random Forest (RF), XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes classification techniques were used to diagnose the CKD. The features subset that selected are different and specialised for each classifier. By implementing the proposed method irrelevant features through filter feature selection able to reduce the burden and computational cost for the genetic algorithm. Then, the genetic algorithm able to perform better and select the best subset that able to improve the performance of the classifier with less attributes. The proposed genetic algorithm union filter feature selections improve the performance of the classification algorithm. The accuracy of RF, XGBoost, KNN and SVM can achieve to 100% and NB can achieve to 99.17%. The proposed method successfully improves the performance of the classifier by using less features as compared to other previous work.

Examining Swarm Intelligence-based Feature Selection for Multi-Label Classification

Multi-label classification addresses the issues that more than one class label assigns to each instance. Many real-world multi-label classification tasks are high-dimensional due to digital technologies, leading to reduced performance of traditional multi-label classifiers. Feature selection is a common and successful approach to tackling this problem by retaining relevant features and eliminating redundant ones to reduce dimensionality. There is several feature selection that is successfully applied in multi-label learning. Most of those features are wrapper methods that employ a multi-label classifier in their processes. They run a classifier in each step, which requires a high computational cost, and thus they suffer from scalability issues. Filter methods are introduced to evaluate the feature subsets using information-theoretic mechanisms instead of running classifiers to deal with this issue. Most of the existing researches and review papers dealing with feature selection in single-label data. While, recently multi-label classification has a wide range of real-world applications such as image classification, emotion analysis, text mining, and bioinformatics. Moreover, researchers have recently focused on applying swarm intelligence methods in selecting prominent features of multi-label data. To the best of our knowledge, there is no review paper that reviews swarm intelligence-based methods for multi-label feature selection. Thus, in this paper, we provide a comprehensive review of different swarm intelligence and evolutionary computing methods of feature selection presented for multi-label classification tasks. To this end, in this review, we have investigated most of the well-known and state-of-the-art methods and categorize them based on different perspectives. We then provided the main characteristics of the existing multi-label feature selection techniques and compared them analytically. We also introduce benchmarks, evaluation measures, and standard datasets to facilitate research in this field. Moreover, we performed some experiments to compare existing works, and at the end of this survey, some challenges, issues, and open problems of this field are introduced to be considered by researchers in the future.