Optimization Based Feature Selection Research Articles

Artificial Intelligence Based Customer Churn Prediction Model for Business Markets.

The introduction of artificial intelligence (AI) and machine learning (ML) technologies in recent years has resulted in improved company performance. Customer churn forecast is a difficult problem in many corporate sectors, particularly the telecommunications industry. Because customer churns have a direct impact on a company's total revenue, telecommunications firms have begun to develop 76 models to reduce churns at an earlier stage. Previous research has revealed that AI and ML models are effective CCP solutions. According to this viewpoint, this study proposes a unique AI-based CCP model for Telecommunication Business Markets (AICCP-TBM). The AICCP-TBM model's purpose is to control the existence of churners and non-churners in the telecom sector. The proposed AICCP-TBM model employs a Chaotic Salp Swarm Optimization-based Feature Selection (CSSO-FS) method for the best feature assortment. In addition, a Fuzzy Rule-based Classifier(FRC) is used to distinguish between client churners and non-churners. A technique known as Quantum Behaved Particle Swarm Optimization (QPSO) is used to pick the membership functions for the FRC model in order to improve the classification performance of the FRC model. The performance of the AICCP-TBM model is validated using a benchmark CCP dataset and the experimental results are reviewed from several angles. In relations of presentation, the imitation consequences demonstrated that the AICCP-TBM model surpassed the most recent state-of-the-art CPP models. The suggested AICCP-TBM method's comparative accuracy was thoroughly tested on the three datasets used. Using datasets 1-3, this technique obtained better levels of accuracy, with the maximum attainable values being 97.25 %, 97.5 % and 94.33 %. The simulation results for the AICCP-TBM model demonstrated improved prediction performance.

Intelligent Intrusion Detection Scheme for Smart Power-Grid Using Optimized Ensemble Learning on Selected Features

The smart grid has gained a reputation as the advanced paradigm of the power grid. It is a complicated cyber-physical system that combines information and communication technology (ICT) with a traditional grid that can remotely control operations. It provides the medium for exchanging real-time data between the company and users through the advanced metering infrastructure (AMI) and smart meters. However, smart grids have many security and privacy concerns, such as intruding sensitive data, firmware hijacking, and modifying data due to the high reliance on ICT. To protect the power-grid system from these counteracts and for reliable and efficient power distribution, early and accurate identification of these issues needs to be addressed. The intrusion detection in a smart grid system plays an essential role in providing a secure service and transmitting the high priority alert message to the system admin about the detection of adversary attacks. This paper proposes an intelligent intrusion detection scheme to accurately classify various attacks on smart power grid systems. The proposed scheme used the binary grey wolf optimization-based feature selection. It optimized the ensemble classification approach to learn the non-linear, overlapping, and complex electrical grid features taken from publicly available Mississippi State University and Oak Ridge National Laboratory (MSU-ORNL) dataset. The experimental results using a 10-fold cross-validation setup and selected feature subset for two class and three class problems reveal the proposed method's promising performance. Further, the significantly superior performance compared to the existing benchmark methods justified the robustness of the proposed scheme.

Cognitive load detection using circulant singular spectrum analysis and Binary Harris Hawks Optimization based feature selection

Cognitive load detection during the mental assignment of neural activity is necessary because it helps to understand the brain’s response to stimuli. An electroencephalogram (EEG) can be used to identify cognitive load during mental arithmetic activities. EEG data was collected from public databases such as the mental arithmetic task (MAT) and simultaneous task workload (STEW). In this manuscript, short-term EEG signals were used to detect cognitive load. Circulant singular spectrum analysis (C-SSA) was used to decompose the EEG signals into intrinsic mode functions (IMF’s). After that, we extract the entropy based features from the IMF’s. For feature selection, optimization algorithms were used, namely Binary Grey Wolf Optimization (BGWO), Binary Harris Hawks Optimization (BHH0), and Binary Differential Evolution (BDE). Furthermore, supervised machine learning methods, namely K-nearest neighbour (KNN) and support vector machine (SVM), were employed to classify selected features based on performance metrics like accuracy (ACC %), sensitivity (SEN), specificity (SPE), precision (PRE), and F-Score (F-S). C-SSA for decomposition and optimization algorithms for feature selection are novel techniques for cognitive load detection. The proposed BHHO-KNN technique achieves the best classification accuracy of 96.88% and 95.28%, individually, for the STEW and MAT datasets. The conclusions of the experiments showed that the proposed technique is more precise at detecting cognitive load compared to existing methods.

Chicken Swarm-Based Feature Subset Selection with Optimal Machine Learning Enabled Data Mining Approach

Data mining (DM) involves the process of identifying patterns, correlation, and anomalies existing in massive datasets. The applicability of DM includes several areas such as education, healthcare, business, and finance. Educational Data Mining (EDM) is an interdisciplinary domain which focuses on the applicability of DM, machine learning (ML), and statistical approaches for pattern recognition in massive quantities of educational data. This type of data suffers from the curse of dimensionality problems. Thus, feature selection (FS) approaches become essential. This study designs a Feature Subset Selection with an optimal machine learning model for Educational Data Mining (FSSML-EDM). The proposed method involves three major processes. At the initial stage, the presented FSSML-EDM model uses the Chicken Swarm Optimization-based Feature Selection (CSO-FS) technique for electing feature subsets. Next, an extreme learning machine (ELM) classifier is employed for the classification of educational data. Finally, the Artificial Hummingbird (AHB) algorithm is utilized for adjusting the parameters involved in the ELM model. The performance study revealed that FSSML-EDM model achieves better results compared with other models under several dimensions.

Binary Particle Swarm Optimization-Based Feature Selection for Predicting the Class of the Knee Angle from EMG Signals in Lower Limb Movements

Binary Particle Swarm Optimization-Based Feature Selection for Predicting the Class of the Knee Angle from EMG Signals in Lower Limb Movements

Hybrid Rough Set With Black Hole Optimization-Based Feature Selection Algorithm for Protein Structure Prediction

In this paper, a new approach for hybridizing Rough Set Quick Reduct and Relative Reduct approaches with Black Hole optimization algorithm is proposed. This algorithm is inspired of black holes. A black hole is a region of spacetime where the gravitational field is so strong that nothing— not even light— that enters this region can ever escape from it. Every black hole has a mass and charge. In this Algorithm, each solution of problem is considered as a black hole and gravity force is used for global search and the electrical force is used for local search. The proposed algorithm is compared with leading algorithms such as, Rough Set Quick Reduct, Rough Set Relative Reduct, Rough Set particle swarm optimization based Quick Reduct, Rough Set based PSO Relative Reduct, Rough Set Harmony Search based Quick Reduct, and Rough Set Harmony Search based Relative Reduct.

Intelligent Feature Subset Selection with Machine Learning Based Detection and Mitigation of DDoS Attacks in 5G Environment

The fifth-generation (5G) technology is anticipated to permit connectivity to billions of devices, called the Internet of Things (IoT). The primary benefit of 5G is that it has maximum bandwidth and can drastically expand service beyond cell phones to standard internet service for conventionally fixed connectivity to homes, offices, factories, etc. But IoT devices will unavoidably be the primary target of diverse kinds of cyberattacks, notably distributed denial of service (DDoS) attacks. Since the conventional DDoS mitigation techniques are ineffective for 5G networks, machine learning (ML) approaches find helpful to accomplish better security. With this motivation, this study resolves the network security issues posed by network devices in the 5G networks and mitigates the harmful effects of DDoS attacks. This paper presents a new pigeon-inspired optimization-based feature selection with optimal functional link neural network (FLNN), PIOFS-OFLNN model for mitigating DDoS attacks in the 5G environment. The proposed PIOFS-OFLNN model aims to detect DDoS attacks with the inclusion of feature selection and classification processes. The proposed PIOFS-OFLNN model incorporates different techniques such as pre-processing, feature selection, classification, and parameter tuning. In addition, the PIOFS algorithm is employed to choose an optimal subset of features from the pre-processed data. Besides, the OFLNN based classification model is applied to determine DDoS attacks where the Rat Swarm Optimizer (RSO) parameter tuning takes place to adjust the parameters involved in the FLNN model optimally. FLNN is a low computational interconnectivity higher cognitive neural network. There are still no hidden layers. FLNN’s input vector is operationally enlarged to produce non-linear remedies. More details can be accessed application of Nature-Inspired Method to Odia Written by hand Number system Recognition. To validate the improved DDoS detection performance of the proposed model, a benchmark dataset is used.

Artificial intelligence enabled intrusion detection systems for cognitive cyber-physical systems in industry 4.0 environment.

In recent days, Cognitive Cyber-Physical System (CCPS) has gained significant interest among interdisciplinary researchers which integrates machine learning (ML) and artificial intelligence (AI) techniques. This era is witnessing a rapid transformation in digital technology and AI where brain-inspired computing-based solutions will play a vital role in industrial informatics. The application of CCPS with brain-inspired computing in Industry 4.0 will create a significant impact on industrial evolution. Though the CCPSs in industrial environment offer several merits, security remains a challenging design issue. The rise of artificial intelligence AI techniques helps to address cybersecurity issues related to CCPS in industry 4.0 environment. With this motivation, this paper presents a new AI-enabled multimodal fusion-based intrusion detection system (AIMMF-IDS) for CCPS in industry 4.0 environment. The proposed model initially performs the data pre-processing technique in two ways namely data conversion and data normalization. In addition, improved fish swarm optimization based feature selection (IFSO-FS) technique is used for the appropriate selection of features. The IFSO technique is derived by the use of Levy Flight (LF) concept into the searching mechanism of the conventional FSO algorithm to avoid the local optima problem. Since the single modality is not adequate to accomplish enhanced detection performance, in this paper, a weighted voting based ensemble model is employed for the multimodal fusion process using recurrent neural network (RNN), bi-directional long short term memory (Bi-LSTM), and deep belief network (DBN), depicts the novelty of the work. The simulation analysis of the presented model highlighted the improved performance over the recent state of art techniques interms of different measures.

Feature Selection with Stacked Autoencoder Based Intrusion Detection in Drones Environment

The Internet of Drones (IoD) offers synchronized access to organized airspace for Unmanned Aerial Vehicles (known as drones). The availability of inexpensive sensors, processors, and wireless communication makes it possible in real time applications. As several applications comprise IoD in real time environment, significant interest has been received by research communications. Since IoD operates in wireless environment, it is needed to design effective intrusion detection system (IDS) to resolve security issues in the IoD environment. This article introduces a metaheuristics feature selection with optimal stacked autoencoder based intrusion detection (MFSOSAE-ID) in the IoD environment. The major intention of the MFSOSAE-ID technique is to identify the occurrence of intrusions in the IoD environment. To do so, the proposed MFSOSAE-ID technique firstly pre-processes the input data into a compatible format. In addition, the presented MFSOSAE-ID technique designs a moth flame optimization based feature selection (MFOFS) technique to elect appropriate features. Moreover, firefly algorithm (FFA) with stacked autoencoder (SAE) model is employed for the recognition and classification of intrusions in which the SAE parameters are optimally tuned with utilize of FFA. The performance validation of the MFSOSAE-ID model was tested utilizing benchmark dataset and the outcomes implied the promising performance of the MFSOSAE-ID model over other techniques with maximum accuracy of 99.72%.

A Blockchain-Based Architecture for Enabling Cybersecurity in the Internet-of-Critical Infrastructures

Due to the drastic increase in the number of critical infrastructures like nuclear plants, industrial control systems (ICS), transportation, it becomes highly vulnerable to several attacks. They become the major targets of cyberattacks due to the increase in number of interconnections with other networks. Several research works have focused on the design of intrusion detection systems (IDS) using machine learning (ML) and deep learning (DL) models. At the same time, Blockchain (BC) technology can be applied to improve the security level. In order to resolve the security issues that exist in the critical infrastructures and ICS, this study designs a novel BC with deep learning empowered cyber-attack detection (BDLE-CAD) in critical infrastructures and ICS. The proposed BDLE-CAD technique aims to identify the existence of intrusions in the network. In addition, the presented enhanced chimp optimization based feature selection (ECOA-FS) technique is applied for the selection of optimal subset of features. Moreover, the optimal deep neural network (DNN) with search and rescue (SAR) optimizer is applied for the detection and classification of intrusions. Furthermore, a BC enabled integrity checking scheme (BEICS) has been presented to defend against the misrouting attacks. The experimental result analysis of the BDLE-CAD technique takes place and the results are inspected under varying aspects. The simulation analysis pointed out the supremacy of the BDLE-CAD technique over the recent state of art techniques with the of 92.63%.

An Enhanced Binary Multiobjective Hybrid Filter-Wrapper Chimp Optimization Based Feature Selection Method for COVID-19 Patient Health Prediction

This work aims to discover the relevant factors to predict the health condition of COVID-19 patients by employing a fresh and enhanced binary multi-objective hybrid filter-wrapper chimp optimization (EBMOChOA-FW) based feature selection (FS) approach. FS is a preprocessing approach that has been highly fruitful in medical applications, as it not only reduces dimensionality but also allows us to understand the origins of an illness. Wrappers are computationally expensive but have excellent classification performance, whereas filters are recognized as quick techniques, although they are less accurate. This study presents an advanced binary multi-objective chimp optimization method based on the hybridization of filter and wrapper for the FS task using two archives. In exceptional instances, the initial ChOA version becomes stuck at the local optima. As a result, a novel ChOA termed EBMOChOA is developed here by integrating the Harris Hawk Optimization (HHO) into the original ChOA to improve the optimizer’s search capabilities and broaden the usage sectors. The location change step in the ChOA optimizer is separated into three parts: modifying the population using HHO to produce an HHO-based population; creating hybrid entities according to HHO-based and ChOA-based individuals; and altering the search agent in the light of greedy technique and ChOA’s tools. The effectiveness of the EBMOChOA-FW is proven by comparing it to five other well-known algorithms on nine different benchmark datasets. Then its strengths are applied to three real-world COVID-19 datasets to predict the health condition of COVID-19 patients.

Particle Swarm Optimization Based Feature Selection for Educational Data Mining

Particle Swarm Optimization Based Feature Selection for Educational Data Mining

Intrusion Detection System for Big Data Analytics in IoT Environment

In the digital area, Internet of Things (IoT) and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal traffic. Though IoT networks are popular and widely employed in real world applications, security in IoT networks remains a challenging problem. Conventional intrusion detection systems (IDS) cannot be employed in IoT networks owing to the limitations in resources and complexity. Therefore, this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning (IMFSDL) based classification model, called IMFSDL-IDS for IoT networks. The proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages: data transformation and data normalization. To manage big data, Hadoop ecosystem is employed. Besides, the IMFSDL-IDS model includes a hill climbing with moth flame optimization (HCMFO) for feature subset selection to reduce the complexity and increase the overall detection efficiency. Moreover, the beetle antenna search (BAS) with variational autoencoder (VAE), called BAS-VAE technique is applied for the detection of intrusions in the feature reduced data. The BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification performance. To validate the intrusion detection performance of the IMFSDL-IDS system, a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct aspects. The resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25% and 97.39% on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.

A Decomposition-Based Multiobjective Clonal Selection Algorithm for Hyperspectral Image Feature Selection

Feature selection is an effective way to handle the strong correlation of hyperspectral image data by screening the significant features, and is generally accepted to be a multiobjective optimization problem. Nevertheless, due to the randomness of the strategies and the ambiguity of the optimization directions, the existing multiobjective evolutionary optimization based feature selection methods can suffer from inefficient search and loss of search space with promising solutions when faced with the high-dimensional and multi-peak search space. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) employs a decomposition framework to provide exact guidance for the optimization directions. Unfortunately, random operators are still used, leading to inadequate local optimization. Thus, evolutionary strategies with search preference such as clonal selection may be necessary for local search. In this paper, a novel decomposition-based multiobjective clonal selection algorithm for feature selection (MOCSA/D_FS) is proposed to obtain a feature subset with a superior classification performance. In MOCSA/D_FS, the information entropy and the ratio of the relative scatter value and mutual information are utilized as two objective functions to evaluate the information amount and redundancy. A series of subproblems are then obtained by decomposing the multiobjective problem through weight vectors, with an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm constraint used to balance the search space. Subsequently, a clonal selection method with search space preference performs a detailed local search on each subproblem, which can fully exploit the potential optimal space. The effectiveness and generalizability of the proposed method was confirmed by experiments on four hyperspectral remote sensing image datasets.

Accurate prediction of glioma grades from radiomics using a multi-filter and multi-objective-based method.

Radiomics, providing quantitative data extracted from medical images, has emerged as a critical role in diagnosis and classification of diseases such as glioma. One main challenge is how to uncover key disease-relevant features from the large amount of extracted quantitative features. Many existing methods suffer from low accuracy or overfitting. We propose a new method, Multiple-Filter and Multi-Objective-based method (MFMO), to identify predictive and robust biomarkers for disease diagnosis and classification. This method combines a multi-filter feature extraction with a multi-objective optimization-based feature selection model, which identifies a small set of predictive radiomic biomarkers with less redundancy. Taking magnetic resonance imaging (MRI) images-based glioma grading as a case study, we identify 10 key radiomic biomarkers that can accurately distinguish low-grade glioma (LGG) from high-grade glioma (HGG) on both training and test datasets. Using these 10 signature features, the classification model reaches training Area Under the receiving operating characteristic Curve (AUC) of 0.96 and test AUC of 0.95, which shows superior performance over existing methods and previously identified biomarkers.

Multi Objective Optimization Based Feature Selection Algorithms for Big Data Analytics: A Review

Dimension reduction or feature selection is thought to be the backbone of big data applications in order to improve performance. Many scholars have shifted their attention in recent years to data science and analysis for real-time applications using big data integration. It takes a long time for humans to interact with big data. As a result, while handling high workload in a distributed system, it is necessary to make feature selection elastic and scalable. In this study, a survey of alternative optimizing techniques for feature selection are presented, as well as an analytical result analysis of their limits. This study contributes to the development of a method for improving the efficiency of feature selection in big complicated data sets.

Multi Objective Optimization Based Feature Selection Algorithms for Big Data Analytics: A Review

Dimension reduction or feature selection is thought to be the backbone of big data applications in order to improve performance. Many scholars have shifted their attention in recent years to data science and analysis for real-time applications using big data integration. It takes a long time for humans to interact with big data. As a result, while handling high workload in a distributed system, it is necessary to make feature selection elastic and scalable. In this study, a survey of alternative optimizing techniques for feature selection are presented, as well as an analytical result analysis of their limits. This study contributes to the development of a method for improving the efficiency of feature selection in big complicated data sets.

RETRACTED ARTICLE: Artificial intelligence assisted Internet of Things based financial crisis prediction in FinTech environment

AbstractFinancial Technology (FinTech) is treated as a distinctive taxonomy which majorly examines the financial technology sectors in a broader set of operations for enterprises by the use of Information Technology (IT) applications. Since the Internet of Things (IoT) is increasing tremendously, artificial intelligence (AI) assisted agile IoT is the way forward for sustainable finance. The deepness of the agile IoT has probably transformed the financial market today, and it may rapidly develop as a dominant tool in the future. The integration of AI and IoT techniques will considerably extract valued financial data and avail better services to the customers. One of the important concepts involved in FinTech is financial crisis prediction (FCP), which is a process of determining the financial status of a company. With this motivation, this paper designs a novel artificial intelligence assisted IoT based FCP (AIAIoT-FCP) model in the FinTech environment. The proposed AIAIoT-FCP model encompasses different stages such as data collection, data preprocessing, feature selection, and classification. At the primary stage, the financial data of the enterprises are collected by the use of the IoT devices such as smartphones and laptops. Besides, a chaotic Henry gas solubility optimization based feature selection (CHGSO-FS) technique is applied to select optimum features. In addition, a deep extreme learning machine (DELM) based classifier is used to determine the class labels of the financial data. Finally, the Nesterov-accelerated Adaptive Moment Estimation (NADAM) based hyperparameter optimizer of the DELM model is involved to boost the classification performance of the DELM model. An extensive simulation analysis is carried out on the benchmark financial dataset to highlight the betterment of the AIAIoT-FCP model. The resultant values portrayed the superior performance of the AIAIoT-FCP model over the state of art techniques in a considerable way.

A hybrid sentiment analysis approach using black widow optimization based feature selection

With the massive development of Internet technologies and e-commerce technology, people rely on the product reviews provided by users through web. Sentiment analysis of online reviews has become a mainstream way for businesses on e-commerce platforms to satisfy the customers. This paper proposes a novel hybrid framework with Black Widow Optimization (BWO) based feature reduction technique which combines the merits of both machine learning and lexicon-based approaches to attain better scalability and accuracy. The scalability problem arises due to noisy, irrelevant and unique features present in the extracted features from proposed approach, which can be eliminated by adopting an effective feature reduction technique. In our proposed BWO approach, without changing the accuracy (90%), the feature-set size is reduced up to 43%. The proposed feature selection technique outperforms other commonly used Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) based feature selection techniques with reduced computation time of 21 sec. Moreover, our sentiment analysis approach is analyzed using performance metrics such as precision, recall, F-measure, and computation time. Many organizations can use these online reviews to make well-informed decisions towards the users’ interests and preferences to enhance customer satisfaction, product quality and to find the aspects to improve the products, thereby to generate more profits.

An Intelligent Metaheuristic Binary Pigeon Optimization-Based Feature Selection and Big Data Classification in a MapReduce Environment

Big Data are highly effective for systematically extracting and analyzing massive data. It can be useful to manage data proficiently over the conventional data handling approaches. Recently, several schemes have been developed for handling big datasets with several features. At the same time, feature selection (FS) methodologies intend to eliminate repetitive, noisy, and unwanted features that degrade the classifier results. Since conventional methods have failed to attain scalability under massive data, the design of new Big Data classification models is essential. In this aspect, this study focuses on the design of metaheuristic optimization based on big data classification in a MapReduce (MOBDC-MR) environment. The MOBDC-MR technique aims to choose optimal features and effectively classify big data. In addition, the MOBDC-MR technique involves the design of a binary pigeon optimization algorithm (BPOA)-based FS technique to reduce the complexity and increase the accuracy. Beetle antenna search (BAS) with long short-term memory (LSTM) model is employed for big data classification. The presented MOBDC-MR technique has been realized on Hadoop with the MapReduce programming model. The effective performance of the MOBDC-MR technique was validated using a benchmark dataset and the results were investigated under several measures. The MOBDC-MR technique demonstrated promising performance over the other existing techniques under different dimensions.