Feature Vector For Classification Research Articles

Classification method of seabed sonar image substrate based on ELM-AdaBoost

The results of sediment classification are affected by the measurement environment. The water depth has a significant impact on the sound propagation, and the acoustic characteristics will be different under different water depths. In addition, seabed topography, sediment types and their distribution, acoustic frequency, equipment types and settings, and suspended substances in water will all affect the original sonar data. In order to obtain accurate classification results, a seabed sonar image classification method based on Elm AdaBoost is proposed. The original sound intensity data is compensated for gain and the anomaly is eliminated. After preprocessing the multi beam sonar data, the image texture feature is extracted from the gray level co-occurrence matrix as the feature vector for classification, and the elm classifier is constructed. The elm is enhanced by AdaBoost. The calculated feature is input into the elm AdaBoost network for sediment classification, and the result is output. Simulation results show that the proposed method effectively improves the accuracy of sediment classification, and verifies the feasibility of this method.

Melanoma classification using generative adversarial network and proximal policy optimization.

In oncology, melanoma is a serious concern, often arising from DNA changes caused mainly by ultraviolet radiation. This cancer is known for its aggressive growth, highlighting the necessity of early detection. Our research introduces a novel deep learning framework for melanoma classification, trained and validated using the extensive SIIM-ISIC Melanoma Classification Challenge-ISIC-2020 dataset. The framework features three dilated convolution layers that extract critical feature vectors for classification. A key aspect of our model is incorporating the Off-policy Proximal Policy Optimization (Off-policy PPO) algorithm, which effectively handles data imbalance in the training set by rewarding the accurate classification of underrepresented samples. In this framework, the model is visualized as an agent making a series of decisions, where each sample represents a distinct state. Additionally, a Generative Adversarial Network (GAN) augments training data to improve generalizability, paired with a new regularization technique to stabilize GAN training and prevent mode collapse. The model achieved an F-measure of 91.836% and a geometric mean of 91.920%, surpassing existing models and demonstrating the model's practical utility in clinical environments. These results demonstrate its potential in enhancing early melanoma detection and informing more accurate treatment approaches, significantly advancing in combating this aggressive cancer.

Social network based link correlation using graph neural network with deep learning architectures for feature vectors prediction and classification

SummaryIn recent years, social network analysis has received a lot of interest. A critical area of research in this field is link prediction. Link prediction is researched for other forms of social networks. Still, because social link networks (SLNs) change over time and depend on the discussed topics, this network has unique difficulties. Recent studies have focused on three main issues: extending link prediction to a dynamic environment, forecasting formation, and destroying network linkages that change over time. Although it is a challenging issue, deep learning (DL) techniques have been demonstrated to increase prediction accuracy significantly. This research proposes a novel approach to link correlation for social networks based on DL architectures in feature vector prediction and classification. Here the input data has been processed for smoothening and normalization with noise removal. Then, the feature vector was predicted using a dynamically structured convolutional radial basis neural network for this data. The expected feature vector has been classified using a stochastic gradient‐based graph neural network. The experimental analysis is carried out for various social network data in terms of accuracy of 98%, precision of 85%, recall of 86%, F‐1 score of 75%, AUC of 72%, and RMSE of 76%.

COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms

BackgroundCOVID-19 is a disease that caused a contagious respiratory ailment that killed and infected hundreds of millions. It is necessary to develop a computer-based tool that is fast, precise, and inexpensive to detect COVID-19 efficiently. Recent studies revealed that machine learning and deep learning models accurately detect COVID-19 using chest X-ray (CXR) images. However, they exhibit notable limitations, such as a large amount of data to train, larger feature vector sizes, enormous trainable parameters, expensive computational resources (GPUs), and longer run-time.ResultsIn this study, we proposed a new approach to address some of the above-mentioned limitations. The proposed model involves the following steps: First, we use contrast limited adaptive histogram equalization (CLAHE) to enhance the contrast of CXR images. The resulting images are converted from CLAHE to YCrCb color space. We estimate reflectance from chrominance using the Illumination–Reflectance model. Finally, we use a normalized local binary patterns histogram generated from reflectance (Cr) and YCb as the classification feature vector. Decision tree, Naive Bayes, support vector machine, K-nearest neighbor, and logistic regression were used as the classification algorithms. The performance evaluation on the test set indicates that the proposed approach is superior, with accuracy rates of 99.01%, 100%, and 98.46% across three different datasets, respectively. Naive Bayes, a probabilistic machine learning algorithm, emerged as the most resilient.ConclusionOur proposed method uses fewer handcrafted features, affordable computational resources, and less runtime than existing state-of-the-art approaches. Emerging nations where radiologists are in short supply can adopt this prototype. We made both coding materials and datasets accessible to the general public for further improvement. Check the manuscript’s availability of the data and materials under the declaration section for access.

Real driving environment EEG-based detection of driving fatigue using the wavelet scattering network

BackgroundDriving fatigue is one of the main factors leading to traffic accidents. So, it is necessary to detect driver fatigue accurately and quickly. New methodTo precisely detect driving fatigue in a real driving environment, this paper adopts a classification method for driving fatigue based on the wavelet scattering network (WSN). Firstly, electroencephalogram (EEG) signals of 12 subjects in the real driving environment are collected and categorized into two states: fatigue and awake. Secondly, the WSN algorithm extracts wavelet scattering coefficients of EEG signals, and these coefficients are used as input in support vector machine (SVM) as feature vectors for classification. ResultsThe results showed that the average classification accuracy of 12 subjects reached 99.33%; the average precision rate reached 99.28%; the average recall rate reached 98.27%; the average F1 score reached 98.74%; and the average classification accuracy of the public data set SEED-VIG reached 99.39%. The average precision, recall rate and F1 score reached 99.27%, 98.41% and 98.83% respectively. Comparison with existing methodsIn addition, the WSN algorithm is compared with traditional convolutional neural network (CNN), Sparse-deep belief networks (SDBN), Spatio-temporal convolutional neural networks (STCNN), Long short-term memory (LSTM), and other methods, and it is found that WSN has higher classification accuracy. ConclusionFurthermore, this method has good versatility, providing excellent recognition effect on small sample data sets, and fast running time, making it convenient for real-time online monitoring of driver fatigue. Therefore, the WSN algorithm is promising in efficiently detecting driving fatigue state of drivers in real environments, contributing to improved traffic safety.

Research on feature vector construction and classification recognition of typical mine microseismic waveforms

In the realm of mining seismology, the identification of microseismic waveforms is a critical step for enhancing the precision of microseismic event localization. Addressing the complexities and diverse sources of noise in subterranean mining environments, this study has developed a novel methodology for the automated categorization and recognition of characteristic microseismic waveforms. Initially, a comprehensive database for mining-induced microseismic waveforms was established, catering to their non-stationary and nonlinear attributes. Based on the distinct features of different microseismic waveforms, this study introduces an approach for classification and identification that relies on waveform characteristics. A feature selection model, KNN-LOO (K-Nearest Neighbor - Leave-One-Out), was employed to optimize the feature vectors, in addition to the utilization of Principal Component Analysis (PCA) for dimensionality reduction of the feature vectors. Ultimately, an enhanced BT-SVM (Binary Tree-Support Vector Machine) model was developed to accomplish the automated classification and identification of mining-induced microseismic waveforms. Experimental results indicate that, compared to conventional SVM classification methods, the proposed methodology not only exhibits theoretical and mechanistic advancements but also significantly boosts performance in practical applications. Specifically, the average misclassification rate for each category of waveforms is reduced to less than 5%, and the overall accuracy has increased by 20%. This considerably elevates both the accuracy and speed of microseismic waveform classification and recognition, thereby providing valuable insights for monitoring and analyzing microseismic activities within mining operations as well as enhancing the safety protocols for mineral extraction.

A Sparse Local Binary Pattern extraction algorithm applied to event sensor data for object classification

Recently, new sensors with active pixels were brought to market. These sensors export local variations of light intensity in the form of asynchronous events with low latency. Since the data output format is a stream of addressable events and not a complete image of light intensities, new algorithms are required for known problems in the field of Computer Vision, such as segmentation, VO, SLAM, object, and scene recognition. There are some proposed methodologies for object recognition using conventional methods, convolutional neural networks, and third-generation neural networks based on spikes. However, convolutional neural networks and spike neural networks require specific hardware for processing, hard to miniaturize. Also, several traditional Computer Vision operators and feature descriptors have been neglected in the context of event sensors and could contribute to lighter methodologies in object recognition. This paper proposes an algorithm for local binary pattern extraction in sparse structures, typically found in this context. This paper also proposes two methodologies using local binary patterns to captures with event-based sensors for object recognition. The first methodology exploits the known motion performed by the sensor, while the second is motion agnostic. It is demonstrated experimentally that the LBP operator is a fast and light alternative that enables variable reduction using PCA in some cases. The experiments also show that it is possible to reduce the final feature vector for classification by up to 99,73% when compared to conventional methods considered state-of-the-art while maintaining comparable accuracy.

Fault diagnosis of Rolling Bearing for Motor Based on LSTM-EEMD and Genetic Optimization

In the process of ensemble empirical mode decomposition (EEMD) for motor rolling bearing time series, if the classifier is trained directly using the eigenvalues extracted from the pattern components, there are two shortcomings leading to the reduction of fault identification accuracy as follows: decomposition has serious endpoint effects; the correlation between extracted features lead to the confusion of the fault feature vector classification boundary. Aiming at the problems, in this paper, a fault diagnosis model was built. Firstly, LSTM is used to extend the original data to reduce the divergence degree of the endpoint. Secondly, correlation analysis and dimensionality reduction were carried out for the extracted features to reduce the feature dimension. Finally, the eigenvalues were weighted and genetically optimized to enhance the boundary of different eigenvectors. The experimental results validated by data sets showed that the accuracy of fault diagnosis would be improved by using LSTM extension and genetic optimization feature vector.

Fault-Diagnosis Method for Rotating Machinery Based on SVMD Entropy and Machine Learning

Rolling bearings and gears are important components of rotating machinery. Their operating condition affects the operation of the equipment. Fault in the accessory directly leads to equipment downtime or a series of adverse reactions in the system, which brings enormous pecuniary loss to the institution. Hence, it is of great significance to detect the operating status of rolling bearings and gears for fault diagnosis. At present, the vibration method is considered to be the most common method for fault diagnosis, a method that analyzes the equipment by collecting vibration signals. However, rotating-machinery fault diagnosis is challenging due to the need to select effective fault feature vectors, use appropriate machine-learning classification methods, and achieve accurate fault diagnosis. To solve this problem, this paper illustrates a new fault-diagnosis method combining successive variational-mode decomposition (SVMD) entropy values and machine learning. First, the simulation signal and the real fault signal are used to analyze and compare the variational-mode decomposition (VMD) and SVMD methods. The comparison results prove that SVMD can be a useful method for fault diagnosis. Then, these two methods are utilized to extract the energy entropy and fuzzy entropy of the gearbox dataset of Southeast University (SEU), respectively. The feature vector and multiple machine-learning classification models are constructed for failure-mode identification. The experimental-analysis results successfully verify the effectiveness of the combined SVMD entropy and machine-learning approach for rotating-machinery fault diagnosis.

A novel facial expression recognition algorithm using geometry β –skeleton in fusion based on deep CNN

Facial expression recognition (FER) methods based on single-source facial data often suffer from reduced accuracy or unpredictability due to facial occlusion or illumination changes. To address this, a new technique called Fusion-CNN is proposed. It improves accuracy by extracting hybrid features using a β-skeleton undirected graph and an ellipse with parameters trained using a 1D-CNN. In addition, a 2D-CNN is trained on the same image. The outputs from these two subnetworks are fused, and their features are concatenated to create a feature vector for classification in a deep neural network. The proposed method is evaluated on four public face datasets: the extended Cohn-Kanade (CK+) dataset, the Japanese Female Facial Expression (JAFFE) dataset, Karolinska Directed Emotional Faces (KDEF), and Oulu-CASIA. The experimental results show that Fusion-CNN outperforms other algorithms, achieving recognition accuracy of 98.22%, 93.07%, 90.30%, and 90.13% for the CK+, JAFFE, KDEF, and Oulu-CASIA datasets, respectively.

Multilayer vectorization to develop a deeper image feature learning model

Computer-Aided Diagnosis (CAD) approaches categorise medical images substantially. Shape, colour, and texture can be problem-specific in medical imagery. Conventional approaches rely largely on them and their relationship, resulting in systems that can't illustrate high-issue domain ideas and have weak prototype generalization. Deep learning techniques deliver an end-to-end model that classifies medical photos thoroughly. Due to the improved medical picture quality and short dataset size, this approach may have high processing costs and model layer restrictions. Multilayer vectorization and the Coding Network-Multilayer Perceptron (CNMP) are merged with deep learning to handle these challenges. This study extracts a high-level characteristic using vectorization, CNN, and conventional characteristics. The model's steps are below. The input picture is vectorized into a few pixels during preprocessing. These pixel images are delivered to a coding network being trained to create high-level classification feature vectors. Medical imaging fundamentals determine picture properties. Finally, neural networks combine the collected features. The recommended technique is tested on ISIC2017 and HIS2828. The model's accuracy is 91% and 92%.

Effect of Heartfulness Meditation Among Long-Term, Short-Term and Non-meditators on Prefrontal Cortex Activity of Brain Using Machine Learning Classification: A Cross-Sectional Study.

Background Meditation is a mental practice with health benefits and may increase activity in the prefrontal cortex of the brain. Heartfulness meditation (HM) is a modified form of rajyoga meditation supported by a unique feature called "yogic transmission." This feasibility study aimed to explorethe effect of HM on electroencephalogram (EEG) connectivity parameters of long-term meditators (LTM), short-term meditators (STM), and non-meditators (NM) with an application of machine learning models and determining classifiermethods that can effectively discriminate between the groups. Materials and methods EEGdata were collected from 34participants. The functional connectivity parameters, correlation coefficient, clustering coefficient, shortest path, and phase locking value were utilized as a feature vector for classification. To evaluate the various states of HM practice, the categorization was done between (LTM, NM) and (STM, NM) using a multitude of machine learning classifiers. Results The classifier's performances were evaluated based on accuracy using 10-fold cross-validation. The results showed that the accuracy of machine learning models ranges from 84% to 100% while classifying LTM and NM, andaccuracyfrom 80% to 93% while classifying STM and NM. It was found that decision trees, support vector machines, k-nearest neighbors, and ensemble classifiers performed better than linear discriminant analysis and logistic regression. Conclusion This is the first study to our knowledgeemploying machine learning for the classification among HM meditators and NM The results indicatedthat machine learning classifierswith EEG functional connectivity as a feature vector could be a viable markerfor accessing meditation ability.

Window length insensitive real-time EMG hand gesture classification using entropy calculated from globally parsed histograms

Electromyography (EMG) signal classification is vital to diagnose musculoskeletal abnormalities and control devices by motion intention detection. Machine learning assists both areas by classifying conditions or motion intentions. This paper proposes a novel window length insensitive EMG classification method utilizing the Entropy feature. The main goal of this study is to show that entropy can be used as the only feature for fast real-time classification of EMG signals of hand gestures. The main goal of this study is to show that entropy can be used as the only feature for fast real-time classification of EMG signals of hand gestures. Additionally, the entropy feature can classify feature vectors of different sliding window lengths without including them in the training data. Many kinds of entropy feature succeeded in electroencephalography (EEG) and electrocardiography (ECG) classification research. However, to the best of our knowledge, the Entropy Feature proposed by Shannon stays untested for EMG classification to this day. All the machine learning models are tested on datasets NinaPro DB5 and the newly collected SingleMyo. As an initial analysis to test the entropy feature, classic Machine Learning (ML) models are trained on the NinaPro DB5 dataset. This stage showed that except for the K Nearest Neighbor (kNN) with high inference time, Support Vector Machines (SVM) gave the best validation accuracy. Later, SVM models trained with feature vectors created by 1 s (200 samples) sliding windows are tested on feature vectors created by 250 ms (50 samples) to 1500 ms (300 samples) sliding windows. This experiment resulted in slight accuracy differences through changing window length, indicating that the Entropy feature is insensitive to this parameter. Lastly, Locally Parsed Histogram (LPH), typical in standard entropy functions, makes learning hard for ML methods. Globally Parsed Histogram (GPH) was proposed, and classification accuracy increased from 60.35% to 89.06% while window length insensitivity is preserved. This study shows that Shannon’s entropy is a compelling feature with low window length sensitivity for EMG hand gesture classification. The effect of the GPH approach against an easy-to-make mistake LPH is shown. A real-time classification algorithm for the entropy features is tested on the newly created SingleMyo dataset.

Fault diagnosis of free-conducting particles within GIL based on vibration signals

Accurate quantitative diagnosis of free-conducting particle faults plays an important role in improving the reliability of the gas insulated line (GIL) system. However, the existing fault diagnosis methods cannot accurately identify the free-conducting particle faults with different quantities and sizes. Motivated by this, this paper proposes a novel fault diagnosis method based on vibration signals, which integrates variational mode decomposition (VMD), self-adapting whale optimization algorithm-multiscale permutation entropy (SAWOA-MPE), and deep forest (DF). First, the raw vibration signals of free-conducting particle faults are decomposed via VMD, and the decomposed signals are reconstructed based on the correlation degree. Afterwards, SAWOA is employed to optimize the critical parameters of MPE, and the optimized MPE is further utilized to extract the fault features of the reconstructed signals. Finally, the extracted feature vectors are trained and tested to construct a valid DF classification model that identifies the free-conducting particle faults. The experimental results indicate that the identification accuracy of the proposed method can reach 99.5%. Moreover, comparative tests based on various feature vector extraction methods and classification models further validate the superiority of the proposed method.

Cumulative foot pressure image recognition via Gabor filters and sparse representation classifier

Analysis of cumulative foot pressure images (CFPIs) can be utilised like other biometrics for personal recognition. This biometric, along with other biometrics such as gait, can be helpful in identification. Also, this overcomes some drawbacks of common biometrics (such as face and gait) in data recording and taking people's foot data without knowing them. In the process of capturing cumulative foot pressure images, the spatial and temporal changes of ground reaction force during one gait cycle are recorded. In this biometric, there are some challenges such as walking at different speeds. In this paper, we present a new approach based on Gabor filters as a feature vector and sparse representation classification (SRC). To reduce the feature dimension, eigenfoot and linear discriminative analysis (LDA) are also used. To obtain translation and rotation invariant representation, a normalisation preprocess is applied. We evaluate the proposed approach via dataset D of the Chinese Academy of Science (CASIA) Gait-Footprint dataset containing the cumulative foot pressure images of 88 persons. The experimental results indicate that the proposed method has higher accuracy than other tested methods, and also is robust to changes in walking speed.

Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier

BackgroundSelf-interacting proteins (SIPs), two or more copies of the protein that can interact with each other expressed by one gene, play a central role in the regulation of most living cells and cellular functions. Although numerous SIPs data can be provided by using high-throughput experimental techniques, there are still several shortcomings such as in time-consuming, costly, inefficient, and inherently high in false-positive rates, for the experimental identification of SIPs even nowadays. Therefore, it is more and more significant how to develop efficient and accurate automatic approaches as a supplement of experimental methods for assisting and accelerating the study of predicting SIPs from protein sequence information.ResultsIn this paper, we present a novel framework, termed GLCM-WSRC (gray level co-occurrence matrix-weighted sparse representation based classification), for predicting SIPs automatically based on protein evolutionary information from protein primary sequences. More specifically, we firstly convert the protein sequence into Position Specific Scoring Matrix (PSSM) containing protein sequence evolutionary information, exploiting the Position Specific Iterated BLAST (PSI-BLAST) tool. Secondly, using an efficient feature extraction approach, i.e., GLCM, we extract abstract salient and invariant feature vectors from the PSSM, and then perform a pre-processing operation, the adaptive synthetic (ADASYN) technique, to balance the SIPs dataset to generate new feature vectors for classification. Finally, we employ an efficient and reliable WSRC model to identify SIPs according to the known information of self-interacting and non-interacting proteins.ConclusionsExtensive experimental results show that the proposed approach exhibits high prediction performance with 98.10% accuracy on the yeast dataset, and 91.51% accuracy on the human dataset, which further reveals that the proposed model could be a useful tool for large-scale self-interacting protein prediction and other bioinformatics tasks detection in the future.

Driving Fatigue Detection with Three Non-Hair-Bearing EEG Channels and Modified Transformer Model.

Driving fatigue is the main cause of traffic accidents, which seriously affects people's life and property safety. Many researchers have applied electroencephalogram (EEG) signals for driving fatigue detection to reduce negative effects. The main challenges are the practicality and accuracy of the EEG-based driving fatigue detection method when it is applied on the real road. In our previous study, we attempted to improve the practicality of fatigue detection based on the proposed non-hair-bearing (NHB) montage with fewer EEG channels, but the recognition accuracy was only 76.47% with the random forest (RF) model. In order to improve the accuracy with NHB montage, this study proposed an improved transformer architecture for one-dimensional feature vector classification based on introducing the Gated Linear Unit (GLU) in the Attention sub-block and Feed-Forward Networks (FFN) sub-block of a transformer, called GLU-Oneformer. Moreover, we constructed an NHB-EEG-based feature set, including the same EEG features (power ratio, approximate entropy, and mutual information (MI)) in our previous study, and the lateralization features of the power ratio and approximate entropy based on the strategy of brain lateralization. The results indicated that our GLU-Oneformer method significantly improved the recognition performance and achieved an accuracy of 86.97%. Our framework demonstrated that the combination of the NHB montage and the proposed GLU-Oneformer model could well support driving fatigue detection.

EFAFN: An Efficient Feature Adaptive Fusion Network with Facial Feature for Multimodal Sarcasm Detection

Sarcasm often manifests itself in some implicit language and exaggerated expressions. For instance, an elongated word, a sarcastic phrase, or a change of tone. Most research on sarcasm detection has recently been based on text and image information. In this paper, we argue that most image data input to the sarcasm detection model is redundant, for example, complex background information and foreground information irrelevant to sarcasm detection. Since facial details contain emotional changes and social characteristics, we should pay more attention to the image data of the face area. We, therefore, treat text, audio, and face images as three modalities and propose a multimodal deep-learning model to tackle this problem. Our model extracts the text, audio, and image features of face regions and then uses our proposed feature fusion strategy to fuse these three modal features into one feature vector for classification. To enhance the model’s generalization ability, we use the IMGAUG image enhancement tool to augment the public sarcasm detection dataset MUStARD. Experiments show that although using a simple supervised method is effective, using a feature fusion strategy and image features from face regions can further improve the F1 score from 72.5% to 79.0%.

COVID-19 screening with digital holographic microscopy using intra-patient probability functions of spatio-temporal bio-optical attributes.

We present an automated method for COVID-19 screening using the intra-patient population distributions of bio-optical attributes extracted from digital holographic microscopy reconstructed red blood cells. Whereas previous approaches have aimed to identify infection by classifying individual cells, here, we propose an approach to incorporate the attribute distribution information from the population of a given human subjects' cells into our classification scheme and directly classify subjects at the patient level. To capture the intra-patient distribution information in a generalized way, we propose an approach based on the Bag-of-Features (BoF) methodology to transform histograms of bio-optical attribute distributions into feature vectors for classification via a linear support vector machine. We compare our approach with simpler classifiers directly using summary statistics such as mean, standard deviation, skewness, and kurtosis of the distributions. We also compare to a k-nearest neighbor classifier using the Kolmogorov-Smirnov distance as a distance metric between the attribute distributions of each subject. We lastly compare our approach to previously published methods for classification of individual red blood cells. In each case, the methodology proposed in this paper provides the highest patient classification performance, correctly classifying 22 out of 24 individuals and achieving 91.67% classification accuracy with 90.00% sensitivity and 92.86% specificity. The incorporation of distribution information for classification additionally led to the identification of a singular temporal-based bio-optical attribute capable of highly accurate patient classification. To the best of our knowledge, this is the first report of a machine learning approach using the intra-patient probability distribution information of bio-optical attributes obtained from digital holographic microscopy for disease screening.

Support vector machine based disease classification model employing hasten eagle Cuculidae search optimization

SummaryThe enormous growth in the field of medicine utilizes machine learning for the well‐being and robustness of human beings. The emerging rate of the disease due to urbanization or various other global facts increases the mortality rate of the individual every year. The detection and classification of disease at the proper time helps an individual to be alive and healthy. Various methods of machine learning are accomplished to classify the diseases, but there is a necessity for the enhancement in the classification methods. In this research, the disease classification is performed using the proposed hasten eagle Cuculidae search optimization algorithm based support vector machine classifier (HECSO‐SVM) with the hybrid kernel function. The feature extraction with the statistical and correlation features presents the effective feature vector for classification and the curse of dimensionality is handled using the proposed HECSO algorithm. Initially, the data will be collected and preprocessing is performed and the features are extracted and selected using the optimization techniques. The proposed method effectively reduces the computation time and increases the accuracy of classification. The analysis is made based on the four different datasets such as Parkinson, heart, lung, and hepatitis that obtains an accuracy rate of 94.582%, 94.100%, 93.985%, 94.582%.