Optimization Based Feature Selection Research Articles

Twitter sentiment analysis using hybrid gated attention recurrent network

Sentiment analysis is the most trending and ongoing research in the field of data mining. Nowadays, several social media platforms are developed, among that twitter is a significant tool for sharing and acquiring peoples’ opinions, emotions, views, and attitudes towards particular entities. This made sentiment analysis a fascinating process in the natural language processing (NLP) domain. Different techniques are developed for sentiment analysis, whereas there still exists a space for further enhancement in accuracy and system efficacy. An efficient and effective optimization based feature selection and deep learning based sentiment analysis is developed in the proposed architecture to fulfil it. In this work, the sentiment 140 dataset is used for analysing the performance of proposed gated attention recurrent network (GARN) architecture. Initially, the available dataset is pre-processed to clean and filter out the dataset. Then, a term weight-based feature extraction termed Log Term Frequency-based Modified Inverse Class Frequency (LTF-MICF) model is used to extract the sentiment-based features from the pre-processed data. In the third phase, a hybrid mutation-based white shark optimizer (HMWSO) is introduced for feature selection. Using the selected features, the sentiment classes, such as positive, negative, and neutral, are classified using the GARN architecture, which combines recurrent neural networks (RNN) and attention mechanisms. Finally, the performance analysis between the proposed and existing classifiers is performed. The evaluated performance metrics and the gained value for such metrics using the proposed GARN are accuracy 97.86%, precision 96.65%, recall 96.76% and f-measure 96.70%, respectively.

Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study.

In recent decades, the brain-computer interface (BCI) has emerged as a leading area of research. The feature selection is vital to reduce the dataset's dimensionality, increase the computing effectiveness, and enhance the BCI's performance. Using activity-related features leads to a high classification rate among the desired tasks. This study presents a wrapper-based metaheuristic feature selection framework for BCI applications using functional near-infrared spectroscopy (fNIRS). Here, the temporal statistical features (i.e., the mean, slope, maximum, skewness, and kurtosis) were computed from all the available channels to form a training vector. Seven metaheuristic optimization algorithms were tested for their classification performance using a k-nearest neighbor-based cost function: particle swarm optimization, cuckoo search optimization, the firefly algorithm, the bat algorithm, flower pollination optimization, whale optimization, and grey wolf optimization (GWO). The presented approach was validated based on an available online dataset of motor imagery (MI) and mental arithmetic (MA) tasks from 29 healthy subjects. The results showed that the classification accuracy was significantly improved by utilizing the features selected from the metaheuristic optimization algorithms relative to those obtained from the full set of features. All of the abovementioned metaheuristic algorithms improved the classification accuracy and reduced the feature vector size. The GWO yielded the highest average classification rates (p < 0.01) of 94.83 ± 5.5%, 92.57 ± 6.9%, and 85.66 ± 7.3% for the MA, MI, and four-class (left- and right-hand MI, MA, and baseline) tasks, respectively. The presented framework may be helpful in the training phase for selecting the appropriate features for robust fNIRS-based BCI applications.

Using Particle Swarm Optimization and Artificial Intelligence to Select the Appropriate Characteristics to Determine Volume Fraction in Two-Phase Flows

Global demand for fossil fuels has increased the importance of flow measurement in the oil sector. As a result, a new submarket in the flowmeter business has opened up. To improve the accuracy of gamma-based two-phase flowmeters, this study employs time-feature extraction methods, a particle swarm optimization (PSO) based feature selection system, and an artificial neural network. This article proposes a fraction detection system that uses a 137Cs gamma source, two NaI detectors for recording the photons, and a Pyrex-glass pipe between them. The Monte Carlo N Particle method was used to simulate the geometry mentioned above. Thirteen time-domain features were extracted from the raw data recorded by both detectors. Optimal characteristics were identified with the help of PSO. This procedure resulted in the identification of eight efficient features. The input-output relationship was approximated using a Multi-Layer Perceptron (MLP) neural network. The innovation of the present research is in the use of a feature extraction technique based on the PSO algorithm to determine volume percentages, with results such as: (1) introducing eight appropriate time characteristics in determining volume percentages; (2) achieving an accuracy of less than 0.37 in root mean square error (RMSE) and 0.14 in mean square error (MSE) while predicting the volume fraction of components in a gas-liquid two-phase flow; and (3) reducing the calculation load. Utilizing optimization-based feature selection techniques has allowed for the selection of meaningful inputs, which has decreased the volume of computations while boosting the precision of the presented system.

Enhanced Chimp Optimization-Based Feature Selection with Fuzzy Logic-Based Intrusion Detection System in Cloud Environment

Cloud computing (CC) refers to an Internet-based computing technology in which shared resources, such as storage, software, information, and platform, are offered to users on demand. CC is a technology through which virtualized and dynamically scalable resources are presented to users on the Internet. Security is highly significant in this on-demand CC. Therefore, this paper presents improved metaheuristics with a fuzzy logic-based intrusion detection system for the cloud security (IMFL-IDSCS) technique. The IMFL-IDSCS technique can identify intrusions in the distributed CC platform and secure it from probable threats. An individual sample of IDS is deployed for every client, and it utilizes an individual controller for data management. In addition, the IMFL-IDSCS technique uses an enhanced chimp optimization algorithm-based feature selection (ECOA-FS) method for choosing optimal features, followed by an adaptive neuro-fuzzy inference system (ANFIS) model enforced to recognize intrusions. Finally, the hybrid jaya shark smell optimization (JSSO) algorithm is used to optimize the membership functions (MFs). A widespread simulation analysis is performed to examine the enhanced outcomes of the IMFL-IDSCS technique. The extensive comparison study reported the enhanced outcomes of the IMFL-IDSCS model with maximum detection efficiency with accuracy of 99.31%, precision of 92.03%, recall of 78.25%, and F-score of 81.80%.

Blockage Detection in Centrifugal Pump Using Butterfly Optimization-Based Feature Selection

Blockage Detection in Centrifugal Pump Using Butterfly Optimization-Based Feature Selection

Feature Selection with Optimal Variational Auto Encoder for Financial Crisis Prediction

Financial crisis prediction (FCP) received significant attention in the financial sector for decision-making. Proper forecasting of the number of firms possible to fail is important to determine the growth index and strength of a nation’s economy. Conventionally, numerous approaches have been developed in the design of accurate FCP processes. At the same time, classifier efficacy and predictive accuracy are inadequate for real-time applications. In addition, several established techniques carry out well to any of the specific datasets but are not adjustable to distinct datasets. Thus, there is a necessity for developing an effectual prediction technique for optimum classifier performance and adjustable to various datasets. This paper presents a novel multi-vs. optimization (MVO) based feature selection (FS) with an optimal variational auto encoder (OVAE) model for FCP. The proposed multi-vs. optimization based feature selection with optimal variational auto encoder (MVOFS-OVAE) model mainly aims to accomplish forecasting the financial crisis. For achieving this, the proposed MVOFS-OVAE model primarily pre-processes the financial data using min-max normalization. In addition, the MVOFS-OVAE model designs a feature subset selection process using the MVOFS approach. Followed by, the variational auto encoder (VAE) model is applied for the categorization of financial data into financial crisis or non-financial crisis. Finally, the differential evolution (DE) algorithm is utilized for the parameter tuning of the VAE model. A series of simulations on the benchmark dataset reported the betterment of the MVOFS-OVAE approach over the recent state of art approaches.

Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

In recent times, cities are getting smart and can be managed effectively through diverse architectures and services. Smart cities have the ability to support smart medical systems that can infiltrate distinct events (i.e., smart hospitals, smart homes, and community health centres) and scenarios (e.g., rehabilitation, abnormal behavior monitoring, clinical decision-making, disease prevention and diagnosis postmarking surveillance and prescription recommendation). The integration of Artificial Intelligence (AI) with recent technologies, for instance medical screening gadgets, are significant enough to deliver maximum performance and improved management services to handle chronic diseases. With latest developments in digital data collection, AI techniques can be employed for clinical decision making process. On the other hand, Cardiovascular Disease (CVD) is one of the major illnesses that increase the mortality rate across the globe. Generally, wearables can be employed in healthcare systems that instigate the development of CVD detection and classification. With this motivation, the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment, abbreviated as AIDSS-CDDC technique. The proposed AIDSS-CDDC model enables the Internet of Things (IoT) devices for healthcare data collection. Then, the collected data is saved in cloud server for examination. Followed by, training and testing processes are executed to determine the patient’s health condition. To accomplish this, the presented AIDSS-CDDC model employs data pre-processing and Improved Sine Cosine Optimization based Feature Selection (ISCO-FS) technique. In addition, Adam optimizer with Autoencoder Gated Recurrent Unit (AE-GRU) model is employed for detection and classification of CVD. The experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

Bayesian Deep Learning Enabled Sentiment Analysis on Web Intelligence Applications

In recent times, web intelligence (WI) has become a hot research topic, which utilizes Artificial Intelligence (AI) and advanced information technologies on the Web and Internet. The users post reviews on social media and are employed for sentiment analysis (SA), which acts as feedback to business people and government. Proper SA on the reviews helps to enhance the quality of the services and products, however, web intelligence techniques are needed to raise the company profit and user fulfillment. With this motivation, this article introduces a new modified pigeon inspired optimization based feature selection (MPIO-FS) with Bayesian deep learning (BDL), named MPIO-BDL model for SA on WI applications. The presented MPIO-BDL model initially involved preprocessing and feature extraction take place using Term Frequency—Inverse Document Frequency (TF-IDF) technique to derive a useful set of information from the user reviews. Besides, the MPIO-FS model is applied for the selection of optimal feature subsets, which helps to enhance classification accuracy and reduce computation complexity. Moreover, the BDL model is employed to allocate the proper class labels of the applied user review data. A comprehensive experimental results analysis highlighted the improved classification efficiency of the presented model.

Oppositional Cat Swarm Optimization-Based Feature Selection Approach for Credit Card Fraud Detection.

Credit card fraud has drastically increased in recent times due to the advancements in e-commerce systems and communication technology. Falsified credit card transactions affect the financial status of the companies as well as clients regularly and fraudsters incessantly try to develop new approaches to commit frauds. The recognition of credit card fraud is essential to sustain the trustworthiness of e-payments. Therefore, it is highly needed to design effective and accurate credit card fraud detection (CCFD) techniques. The recently developed machine learning (ML) and deep learning (DL) can be employed for CCFD because of the characteristics of building an effective model to identify fraudulent transactions. In this view, this study presents a novel oppositional cat swarm optimization-based feature selection model with a deep learning model for CCFD, called the OCSODL-CCFD technique. The major intention of the OCSODL-CCFD technique is to detect and classify fraudulent transactions using credit cards. The OCSODL-CCFD technique derives a new OCSO-based feature selection algorithm to choose an optimal subset of features. Besides, the chaotic krill herd algorithm (CKHA) with the bidirectional gated recurrent unit (BiGRU) model is applied for the classification of credit card frauds, in which the hyperparameter tuning of the BiGRU model is performed using the CKHA. To demonstrate the supreme outcomes of the OCSODL-CCFD model, a wide range of simulation analyses were carried out. The extensive comparative analysis highlighted the better outcomes of the OCSODL-CCFD model over the compared ones based on several evaluation metrics.

Butterfly Optimized Feature Selection with Fuzzy C-Means Classifier for Thyroid Prediction

The main task of thyroid hormones is controlling the metabolism rate of humans, the development of neurons, and the significant growth of reproductive activities. In medical science, thyroid disorder will lead to creating thyroiditis and thyroid cancer. The two main thyroid disorders are hyperthyroidism and hypothyroidism. Many research works focus on the prediction of thyroid disorder. To improve the accuracy in the classification of thyroid disorder this paper proposes optimization-based feature selection by using differential evolution with the Butterfly optimization algorithm (DE-BOA). For the classifier fuzzy C-means algorithm (FCM) is used. The proposed DEBOA-FCM is evaluated with parametric metric measures of sensitivity, specificity, and accuracy. In this work, the thyroid disease dataset collected from the machine learning University of California Irvine (UCI) database was used. The accuracy rate for the Differential Evolutionary algorithm got 0.884, the Butterfly optimization algorithm got 0.906, Fuzzy C-Means algorithm got 0.899 and DEBOA + Focused Concept Miner (FCM) proposed work 0.943.

Colliding Bodies Optimization with Machine Learning Based Parkinson’s Disease Diagnosis

Parkinson’s disease (PD) is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly patients. It affect their quality of life drastically and millions of seniors are diagnosed with PD every year worldwide. Several models have been presented earlier to detect the PD using various types of measurement data like speech, gait patterns, etc. Early identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of PD. The recently-emerging Deep Learning (DL) models can leverage the past data to detect and classify PD. With this motivation, the current study develops a novel Colliding Bodies Optimization Algorithm with Optimal Kernel Extreme Learning Machine (CBO-OKELM) for diagnosis and classification of PD. The goal of the proposed CBO-OKELM technique is to identify whether PD exists or not. CBO-OKELM technique involves the design of Colliding Bodies Optimization-based Feature Selection (CBO-FS) technique for optimal subset of features. In addition, Water Strider Algorithm (WSA) with Kernel Extreme Learning Machine (KELM) model is also developed for the classification of PD. CBO algorithm is used to elect the optimal set of features whereas WSA is utilized for parameter tuning of KELM model which altogether helps in accomplishing the maximum PD diagnostic performance. The experimental analysis was conducted for CBO-OKELM technique against four benchmark datasets and the model portrayed better performance such as 95.68%, 96.34%, 92.49%, and 92.36% on Speech PD, Voice PD, Hand PD Meander, and Hand PD Spiral datasets respectively.

Chaotic Metaheuristics with Multi-Spiking Neural Network Based Cloud Intrusion Detection

Cloud Computing (CC) provides data storage options as well as computing services to its users through the Internet. On the other hand, cloud users are concerned about security and privacy issues due to the increased number of cyberattacks. Data protection has become an important issue since the users’ information gets exposed to third parties. Computer networks are exposed to different types of attacks which have extensively grown in addition to the novel intrusion methods and hacking tools. Intrusion Detection Systems (IDSs) can be used in a network to manage suspicious activities. These IDSs monitor the activities of the CC environment and decide whether an activity is legitimate (normal) or malicious (intrusive) based on the established system’s confidentiality, availability and integrity of the data sources. In the current study, a Chaotic Metaheuristics with Optimal Multi-Spiking Neural Network-based Intrusion Detection (CMOMSNN-ID) model is proposed to secure the cloud environment. The presented CMOMSNN-ID model involves the Chaotic Artificial Bee Colony Optimization-based Feature Selection (CABC-FS) technique to reduce the curse of dimensionality. In addition, the Multi-Spiking Neural Network (MSNN) classifier is also used based on the simulation of brain functioning. It is applied to resolve pattern classification problems. In order to fine-tune the parameters relevant to the MSNN model, the Whale Optimization Algorithm (WOA) is employed to boost the classification results. To demonstrate the superiority of the proposed CMOMSNN-ID model, a useful set of simulations was performed. The simulation outcomes inferred that the proposed CMOMSNN-ID model accomplished a superior performance over other models with a maximum accuracy of 99.20%.

Modelling of Metaheuristics with Machine Learning-Enabled Cybersecurity in Unmanned Aerial Vehicles

The adoption and recent development of Unmanned Aerial Vehicles (UAVs) are because of their widespread applications in the private and public sectors, from logistics to environment monitoring. The incorporation of 5G technologies, satellites, and UAVs has provoked telecommunication networks to advance to provide more stable and high-quality services to remote areas. However, UAVs are vulnerable to cyberattacks because of the rapidly expanding volume and poor inbuilt security. Cyber security and the detection of cyber threats might considerably benefit from the development of artificial intelligence. A machine learning algorithm can be trained to search for attacks that may be similar to other types of attacks. This study proposes a new approach: metaheuristics with machine learning-enabled cybersecurity in unmanned aerial vehicles (MMLCS-UAVs). The presented MMLCS-UAV technique mainly focuses on the recognition and classification of intrusions in the UAV network. To obtain this, the presented MMLCS-UAV technique designed a quantum invasive weed optimization-based feature selection (QIWO-FS) method to select the optimal feature subsets. For intrusion detection, the MMLCS-UAV technique applied a weighted regularized extreme learning machine (WRELM) algorithm with swallow swarm optimization (SSO) as a parameter tuning model. The experimental validation of the MMLCS-UAV method was tested using benchmark datasets. This widespread comparison study reports the superiority of the MMLCS-UAV technique over other existing approaches.

Uncertainty Optimization Based Feature Selection Model for Stock Marketing

Uncertainty Optimization Based Feature Selection Model for Stock Marketing

Computer Vision-Based Cashew Nuts Grading System Using Machine Learning Methods

In this paper, a computer vision-based cashew nut grading system has been designed and implemented for classifying different grades of cashew nuts using combined features and machine learning approaches. The important task in the cashew nut grading system is to classify the whole and split down cashew nuts. Since these cashew nuts look very similar from the top view, it is a challenging task to classify the whole cashew nut and split down cashew nuts. Hence, a single-view image of cashew nut has been captured by placing a camera with a distance of 17[Formula: see text]cm (from the right side of the conveyor belt). The captured red, blue and green images are normalized and converted into hue, saturation and value color space. S channel from HSV image is used for segmentation process using Otsu threshold technique. The total numbers of features extracted are 275 and the features are texture (180), color (90), and shape (5). The constrained optimization-based feature selection method is used and 30 features are selected for further process. The Support Vector Machine (SVM) classifier is used for the classification, and the results obtained from different kernel functions are computed and compared. The 8-layer convolutional neural network (CNN) has been developed in this work for classification and to analyze the performance and accuracy. The accuracy of different machine learning classifiers like SVM 1-1, SVM 1-All and CNN model is also evaluated and compared. The overall accuracy obtained by SVM 1-All with kernel function radial basis for classification is 98.93%.

Oppositional poor and rich optimization with deep learning enabled secure internet of drone communication system

As a result of technological advancements and the need for continuous reduction in manufacturing costs, the concept of Internet of Things (IoT), consisting of Unmanned Aerial Vehicles (UAVs), has entered the industrial production units. IoT devices have not only penetrated the day-to-day activities of human beings, but also in defence. In recent years, there is a widespread application of Internet of Drones (IoD) in areas such as television and film shooting, meteorological monitoring, forest fire detection, agricultural monitoring, emergency rescue, etc. In this background, Intrusion Detection System (IDS) plays an important role to effectually secure the IoD network. The current research work develops an Opposition Poor and Rich Optimization-based Feature Selection with Optimal Deep Feed-forward Neural Network (OPRFS-ODFNN) model for intrusion detection in IoD communication system. The aim of the presented OPRFS-ODFNN technique is to accomplish enhanced security in IoD communication system. In order to achieve the objective, OPRFS-ODFNN model initially executes feature scaling as a pre-processing step. Then, OPRFS technique is applied for effective selection of the features. Moreover, Improved Mayfly Optimization (IMFO) is applied with ODFNN model for intrusion detection and classification processes. In order to validate the enhanced performance of the proposed OPRFS-ODFNN method, extensive simulations were conducted.

Crystal Structure Optimization with Deep-Autoencoder-Based Intrusion Detection for Secure Internet of Drones Environment

Drone developments, especially small-sized drones, usher in novel trends and possibilities in various domains. Drones offer navigational inter-location services with the involvement of the Internet of Things (IoT). On the other hand, drone networks are highly prone to privacy and security risks owing to their strategy flaws. In order to achieve the desired efficiency, it is essential to create a secure network. The purpose of the current study is to have an overview of the privacy and security problems that recently impacted the Internet of Drones (IoD). An Intrusion Detection System (IDS) is an effective approach to determine the presence of intrusions in the IoD environment. The current study focuses on the design of Crystal Structure Optimization with Deep-Autoencoder-based Intrusion Detection (CSODAE-ID) for a secure IoD environment. The aim of the presented CSODAE-ID model is to identify the occurrences of intrusions in IoD environment. In the proposed CSODAE-ID model, a new Modified Deer Hunting Optimization-based Feature Selection (MDHO-FS) technique is applied to choose the feature subsets. At the same time, the Autoencoder (AE) method is employed for the classification of intrusions in the IoD environment. The CSO algorithm, inspired by the formation of crystal structures based on the lattice points, is employed at last for the hyperparameter-tuning process. To validate the enhanced performance of the proposed CSODAE-ID model, multiple simulation analyses were performed and the outcomes were assessed under distinct aspects. The comparative study outcomes demonstrate the superiority of the proposed CSODAE-ID model over the existing techniques.

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.