Belief Network Classifier Research Articles

Crop Disease Detection by Deep Joint Segmentation and Hybrid Classification Model: A CAD‐Based Agriculture Development System

ABSTRACTPrecise detection of crop disease at the early stage is a crucial task, which will reduce the spreading of disease by taking preventive measures. The main goal of this research is to propose a hybrid classification system for detecting crop disease by utilising Modified Deep Joint (MDJ) segmentation. The detection of crop diseases involves five stages. They are data acquisition, pre‐processing, segmentation, feature extraction and disease detection. In the initial stage, image data of diverse crops is gathered in the data acquisition phase. According to the work, we are considering Apple and corn crops with benchmark datasets. The input image is subjected to pre‐processing by utilising the median filtering process. Subsequently, the pre‐processed image under goes a segmentation process, where Modified Deep Joint segmentation is proposed in this work. From the segmented image, features like shape, colour, texture‐based features and Improved Median Binary Pattern (IMBP)‐based features are extracted. Finally, the extracted features are given to the hybrid classification system for identifying the crop diseases. The hybrid classification model includes Bidirectional Long Short‐Term Memory (Bi‐LSTM) and Deep Belief Network (DBN) classifiers. The outcome of both the classifiers is the score, which is subjected to an improved score level fusion model, which determines the final detection results. Finally, the performance of the proposed hybrid model is evaluated over existing methods for various metrics. At a training data of 90%, the proposed scheme attained an accuracy of 0.965, while conventional methods achieved less accuracy rates.

Exploring role of prefrontal cortex region of brain in children having ADHD with machine learning: Implications and insights

Objective Attention deficit hyperactivity disorder (ADHD), is a general neurodevelopmental syndrome. This affects both adults and children, causing issues like hyperactivity, inattention, and impulsivity. Diagnosis, typically reliant on patient narratives and questionnaires, can sometimes be inaccurate, leading to distress. We propose utilizing empirical mode decomposition (EMD) for feature extraction and a machine learning (ML) algorithm to categorize ADHD and control. Method Publicly available Kaggle dataset is used for research. The EMD technique decomposes an electroencephalogram (EEG) waveform to 12 intrinsic mode functions (IMFs). Thirty-one statistical parameters are generated over the first 6 IMFs to create an input feature vector for the deep belief network (DBN) classifier. Principal component analysis (PCA) is utilized to reduce dimension. Findings Experimental results are compared on prefrontal cortex channels Fp1 and Fp2. After an in-depth evaluation of all metrics, it is observed that, in patients with ADHD, the prefrontal cortex regulates attention, behavior, and emotion. Our findings align with established neuroscience. The critical functions of the brain, such as organization, planning, attention, and decision making, are performed by the frontal lobe. Novelty Our work provides a novel approach to understanding the disorder’s underlying neurobiological mechanisms. It has the potential to deepen our understanding of the condition, improve diagnostic accuracy, personalize treatment methods, and, ultimately, improve outcomes for those affected.

Vehicle Detection and Classification via YOLOv8 and Deep Belief Network over Aerial Image Sequences

Vehicle detection and classification are the most significant and challenging activities of an intelligent traffic monitoring system. Traditional methods are highly computationally expensive and also impose restrictions when the mode of data collection changes. This research proposes a new approach for vehicle detection and classification over aerial image sequences. The proposed model consists of five stages. All of the images are preprocessed in the first stage to reduce noise and raise the brightness level. The foreground items are then extracted from these images using segmentation. The segmented images are then passed onto the YOLOv8 algorithm to detect and locate vehicles in each image. The feature extraction phase is then applied to the detected vehicles. The extracted feature involves Scale Invariant Feature Transform (SIFT), Oriented FAST and Rotated BRIEF (ORB), and KAZE features. For classification, we used the Deep Belief Network (DBN) classifier. Based on classification, the experimental results across the three datasets produced better outcomes; the proposed model attained an accuracy of 95.6% over Vehicle Detection in Aerial Imagery (VEDAI) and 94.6% over Vehicle Aerial Imagery from a Drone (VAID) dataset, respectively. To compare our model with the other standard techniques, we have also drawn a comparative analysis with the latest techniques in the research.

A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches.

Diabetes in humans is a rapidly expanding chronic disease and a major crisis in modern societies. The classification of diabetics is a challenging and important procedure that allows the interpretation of diabetic data and diagnosis. Missing values in datasets can impact the prediction accuracy of the methods for the diagnosis. Due to this, a variety of machine learning techniques has been studied in the past. This research has developed a new method using machine learning techniques for diabetes risk prediction. The method was developed through the use of clustering and prediction learning techniques. The method uses Singular Value Decomposition for missing value predictions, a Self-Organizing Map for clustering the data, STEPDISC for feature selection, and an ensemble of Deep Belief Network classifiers for diabetes mellitus prediction. The performance of the proposed method is compared with the previous prediction methods developed by machine learning techniques. The results reveal that the deployed method can accurately predict diabetes mellitus for a set of real-world datasets.

Deep Belief Neural Network Based Automatic CVD Prediction Using Adaptive Sliding Window Technique

INTRODUCTION: Cardiac Vascular Disease (CVD) is determined to be the most prevailing disease all over the globe specifically in the case of elderly persons. Among various cardiac disease, CVD account for major mortality all over the globe. Diagnosis of cardiac disease at an early stage is mandatory to reduce the rate of mortality. Still, there is no availability of skilled specialists even in case of developed countries for accurate diagnosis. OBJECTIVES: Achieving automated and accurate diagnosis, computer vision based methods that functions with the help of AI techniques are focused on by researchers. In this current research automated CVD prediction model is designed using a deep learning approach. METHODS: ECG image dataset is utilized in this proposed CVD prediction model. Initially, the Non-ST-elevation myocardial infarction (NSTEMI) ECG data collected from the healthcare centre is taken as input. This input ECG image is converted into a signal and further, it is segmented using the sliding window segmentation technique. Then, using segmented signal QRS peak detection is achieved using Elephant Herd Optimization (EHO) algorithm. From the peak, detected signal features are extracted using Heart Rate Variability (HRV) analysis. Following that the extracted features are sent as input into the Deep Belief Network (DBN) classifier to predict CVD patients. RESULTS: The proposed CVD prediction model is implemented and some of the performance metrics are calculated. Accuracy, error, precision, sensitivity and specificity attained by the proposed model using the second dataset are 95%, 5%. 96%, 94% and 96%. Results showed that the functioning of proposed CVD prediction model is better when compared with other existing techniques. CONCLUSION: Based on this analysis it can be revealed that accurate and timely CVD prediction can be achieved with a lessor error rate. Further, this proposed model can be used in real time healthcare application by collecting NSTEMI ECG signal from patients.

SAR-BSO meta-heuristic hybridization for feature selection and classification using DBNover stream data

Advancements in cloud technologies have increased the infrastructural needs of data centers due to storage needs and processing of extensive dimensional data. Many service providers envisage anomaly detection criteria to guarantee availability to avoid breakdowns and complexities caused due to large-scale operations. The streaming log data generated is associated with multi-dimensional complexity and thus poses a considerable challenge to detect the anomalies or unusual occurrences in the data. In this research, a hybrid model is proposed that is motivated by deep belief criteria and meta-heuristics. Using Search-and-Rescue—BrainStorm Optimization (SAR-BSO), a hybrid feature selection (FS) and deep belief network classifier is used to localize and detect anomalies for streaming data logs. The significant contribution of the research lies in FS, which is carried out using SAR-BSO which increases the detection power of the model as it selects the most significant variables by minimizing redundant features. The evaluation of accuracy is efficiently improved when compared with the predictable methods, such as Extract Local Outlier Factor (ELOF), Track-plus, Hybrid Distributed Batch Stream (HDBS), IForestASD, DBN, BSO-based Feature Selection with DBN, Genetic Algorithm-Deep Belief Network (GA-DBN), Mutual Information-Deep Belief Network (MI-DBN), information entropy-Deep Belief Network(I + DBN), Flat Field-Deep Belief Network (FF + DBN), African Vulture Optimization Algorithm-Deep Belief Network(AVOA + DBN), Gorilla Troop Optimizer-Deep Belief Network(GTO-DBN), and SARO-based Feature Selection with DBN. Further, the accurate detection of the anomalies in the data stream is established by the Deep Belief Neural Network (DBN) classifier. The model’s efficacy is determined using Apache, Hadoop, HDFS, Spark, and Linux datasets and evaluated against existing similar models. The model efficiency is provided using multiple evaluation metrics and is found effective. From the experimentation, the accuracy of the proposed model is found to be 93.3, 95.4, 93.6, 94.2, and 93.5% respectively for the dataset such as Apache, Hadoop, HDFS, spark, and Linux. This enhancement in accuracy is due to the selection of optimal features by the proposed SAR-BSO algorithm.

CSChO-Deep MaxNet: Cat swam chimp optimization integrated deep maxout network for heart disease detection

Early heart disease identification is of tremendous importance to minimize mortality. As, the medical data is gradually increases, it is more complicate to analyze and process the data. The detection results generated by the existing methods are inaccurate. This research design a Cat Swam Chimp Optimization (CSChO)-based Deep maxout network for detecting the heart disease with medical data. The data is pre-processed using missing data imputation and log transformation. After that, feature fusion mechanism is achieved using Kendall tau distance measure with Deep Belief network (DBN) classifier. Utilizing the features, the suggested CSChO algorithm trained deep maxout network is used to diagnose disease. The Cat Swarm Optimization (CSO) and Chimp Optimization Algorithm (ChOA) are combined to create the CSChO. Testing accuracy, sensitivity, and specificity metrics for training samples show the suggested technique to perform better with values of 0.948, 0.949, and 0.919 respectively, by Hungary dataset.

Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer Diagnosis.

Histopathological images are commonly used imaging modalities for breast cancer. As manual analysis of histopathological images is difficult, automated tools utilizing artificial intelligence (AI) and deep learning (DL) methods should be modelled. The recent advancements in DL approaches will be helpful in establishing maximal image classification performance in numerous application zones. This study develops an arithmetic optimization algorithm with deep-learning-based histopathological breast cancer classification (AOADL-HBCC) technique for healthcare decision making. The AOADL-HBCC technique employs noise removal based on median filtering (MF) and a contrast enhancement process. In addition, the presented AOADL-HBCC technique applies an AOA with a SqueezeNet model to derive feature vectors. Finally, a deep belief network (DBN) classifier with an Adamax hyperparameter optimizer is applied for the breast cancer classification process. In order to exhibit the enhanced breast cancer classification results of the AOADL-HBCC methodology, this comparative study states that the AOADL-HBCC technique displays better performance than other recent methodologies, with a maximum accuracy of 96.77%.

Enhancement of Online Education in Engineering College Based on Mobile Wireless Communication Networks and IOT

The field of Engineering is that which needs a high level of analytical thinking, intuitive knowledge, and technical know-how. The area of communication engineering deals with different components including, wireless mobile services, radio, broadband, web and satellites. There is a rapid decline in the quality of students produced by engineering faculties as a result of sufficient and quality methods and frameworks of student assessment. The production of high-potential engineers is limited by the utilization of old and traditional education methodology and frameworks. The student presentation estimation system in engineering institution is a motionless manual. Usually, the assessment of student’s performance using the traditional system is limited to the use of students’ performance scores, while failing to evaluate their performance based on activities or practical applications. In addition, such systems do not take cognizance of individual knowledge of students that connects to different activities within the learning environment. Recently, engineering institutions have started paying attention to evaluation solutions that are based on wireless networks and Internet of Things (IoT). Therefore, in this study, an automated system has been proposed for the assessment of engineering students. The proposed system is designed based on IoT and wireless communication networks with the aim of improving the process of virtual education. The data used in this study has been collected through the use of different IoT sensors within the premises of the college, and pre-processed using normalization. After the data was pre-processed, it was stored in cloud. In order to enable the classification of student’s activity, an Adaptive Layered Bayesian Belief Network (AL-BBN) classifier is proposed in this work. The student’s scores have been calculated using fuzzy logic, while Multi-Gradient Boosting Decision Tree (MGBDT) was proposed for decision making. The use of python simulation tool is employed in the implementation of the proposed system, and the evaluation of the performance benchmarks was done as well. Based on the findings of the study, the proposed conceptual model outperformed the existing ones in terms of improving the process of online learning.

Comparative analysis of image classification with retrieval system

Currently, the term 'content-based image retrieval' seems to be a highly attentive system for handling the broad image datasets since the data storage mechanisms and image acquisition are becoming the most empowered logic in image processing. The previous CBIR system has been proposed under nonlinear similarity matching measure in a logarithmic scale and informative pattern descriptor has quantified the range of similarity content. This article implements a novel CBIR system that emphasises the classification concept using a deep belief network (DBN) classifier. In this concept, apart from the image retrieval, the used classifier classifies the respective classes of retrieved images. Finally, the proposed local vector pattern (PLVP) with DBN classifier (PLVP-DBN) compares its performance over other conventional retrieval concepts: PLVP-with log similarity, PLVP-without log similarity, and also with neural network (NN) classifier.

A Novel Melspectrogram Snippet Representation Learning Framework for Severity Detection of Chronic Obstructive Pulmonary Diseases

Chronic obstructive pulmonary disease (COPD) is a major public health concern across the world. Since it is an incurable disease, early detection, and accurate diagnosis are very crucial for preventing the progression of the disease. Lung sounds provide reliable and accurate prognoses for identifying respiratory diseases. Recently, Altan et al. recorded 12-channel real-time lung sound dataset, namely RespiratoryDatabase@TR, for five different severity levels of COPD at Antakya State Hospital Turkey, and proposed deep learning frameworks for two-class COPD classification and five-class classification using a deep belief network (DBN) classifier and extreme learning machine (ELM) classifier respectively. A classification accuracy of 95.84% and 94.31% were achieved for two-class and five-class, respectively. In this paper, we have proposed a melspectrogram snippet representation learning framework for both two-class and five-class COPD classification. The proposed framework consists of the following stages: data augmentation and pre-processing, melspectrogram snippet representation generation from lung sound, and fine-tuning of a pre-trained YAMNet. Experimental analysis on the RespiratoryDatabase@TR dataset demonstrates that the proposed framework achieves accuracies of 99.25% and 96.14% for binary and multi-class COPD severity classification, respectively, which are superior to the only existing methods proposed by Altan et al. for severity analysis of COPD using lung sounds.

Hybrid model with optimization tactics for software defect prediction

Defects are frequent in software systems, and they can cause a lot of issues for users. Despite the fact that many studies have been conducted on employing software product metrics to determine defect-prone modules, defect prediction techniques are still worth investigating. Hence, the aim of this work is to provide a unique Software Defect Prediction (SDP) approach that includes four steps like “(a) pre-processing, (b) feature extraction, (c) feature selection and (d) detection.” At first, the input data are given to the pre-processing step, as well as in the feature extraction step; the “statistical features, raw features, higher-order statistical features as well as proposed entropy features” are extracted from the pre-processed data. In addition, the retrieved features are sent into a feature selection step, wherein the appropriate features are selected utilizing a modified chi-square scheme. In the detection step, a hybrid Long Short-Term Memory (LSTM) and Deep Belief Network (DBN) classifiers are used to predict the defects. To provide a more accurate detection, the weights of both DBN and LSTM are optimally tuned via a Self Improved Social Ski-Driver Optimization (SISSDO) algorithm. This proposed SDP model is a beneficial practice for enhancing software quality and reliability. Moreover, the results of the adopted technique are assessed to traditional techniques on the basis of various measures. In particular, the accuracy of the suggested approach for dataset 3 is 5.80%, 6.52%, 5.07%, 7.97%, 5.80%, 9.42%, 9.42%, 10.15%, 2.17%, and 3.62% better than the extant HC[Formula: see text]+[Formula: see text]ALO, HC[Formula: see text]+[Formula: see text]SMO, HC[Formula: see text]+[Formula: see text]CMBO, HC[Formula: see text]+[Formula: see text]SSD, RNN, CNN, NN, Bi-LSTM, HC+SPFCNN, and HC[Formula: see text]+[Formula: see text]CWAR approaches, correspondingly. Moreover, the computation time of the suggested approach is 17.05%, 5.78%, 1.31%, and 50.53% better than the existing HC[Formula: see text]+[Formula: see text]ALO, HC[Formula: see text]+[Formula: see text]SMO, HC[Formula: see text]+[Formula: see text]CMBO, and HC[Formula: see text]+[Formula: see text]SSD approaches, correspondingly.

Online product sentiment analysis using random evolutionary whale optimization algorithm and deep belief network

• Image and text normalization techniques are used to improve the data quality. • Used MLBP , SURF, TF-IDF and LSA techniques for feature extraction. • Used RE-WOA to reduce the dimension of the extracted feature vectors. • Used DBN classifier to classify the user's sentiments of the online products. In the recent decades, the online product sentiment analysis is an emerging research topic that assists the customers to take better decisions on purchasing the products and to achieve better sales of the products. Recently, several machine learning techniques are experimented on many datasets for analyzing the customer's sentiments through online portals. Still, the customers are struggling to obtain the aspect sentiments expressed by other customers, particularly in the amazon websites. Therefore, a novel automated model is proposed in this manuscript for an effective online product sentiment analysis. After collecting the multimodal data from the amazon websites, the image and data normalization techniques are employed for better understanding of the collected data. Further, the feature extraction is performed by utilizing Latent Semantic Analysis (LSA), Term Frequency- Inverse Document Frequency (TF-IDF), Modified Local Binary Pattern (MLBP), and Speeded Up Robust Features (SURF) descriptors for extracting the textual and visual feature vectors from the preprocessed data. Finally, the Random Evolutionary Whale Optimization Algorithm (REWOA) and Deep Belief Network (DBN) classifier are integrated for feature vector optimization and sentiment classification. By using feature optimization, the system complexity and running time of the classifier is improved. The experimental investigation states that the developed REWOA-DBN model achieved 96.86% of classification accuracy, which is better compared to other optimizers and classifiers

Brain MR Images Classification for Alzheimer’s Disease

Alzheimer’s Disease (AD) is the most prevailing type of dementia. The prevalence of AD is estimated to be around 5% after 65 years old and is staggering 30% for more than 85 years old in developed countries. AD destroys brain cells causing people to lose their memory, mental functions and ability to continue daily activities. The findings of this study are likely to aid specialists in their decision-making process by using patients’ Magnetic Resonance Imaging (MRI) to distinguish patients with AD from Normal Control (NC). Performance evolution was applied to 346 Magnetic Resonance images from the Alzheimer's Neuroimaging Initiative (ADNI) collection. The Deep Belief Network (DBN) classifier was used to fulfill classification function. Weights were used to test the proposed method's recognition capacity, and the network was trained with a sample training set. As a result, this study offeres a new method for identifying Alzheimer's disease utilizing automated categorization. In tests, it performed admirably With 98.46% accuracy achieved for AD and NC studied classes when combining Gray Level Co-occurrence Matrix (GLCM) features with a DBN.

Text sentiment analysis on E‐shopping product reviews using chaotic coyote optimized deep belief network approach

AbstractText sentiment analysis is mainly used to the customers benefits. In the existing works, the text sentiment analysis faces more troubles such as, disambiguation (removing unwanted terms), discussions, contrast, intensity, and excessive flections and complex sound structure with less accuracy. In this article, the text sentiment analysis on E‐shopping product using chaotic coyote optimized deep belief network approach is proposed to minimize the troubles in the sentiment analysis and increase the accuracy. The other name of sentiment analysis is subjective analysis. The main objective of this article is “classify the text sentiments according to the polarity (positive and negative) and to increase the accuracy.” Here, the E‐shopping Kaggle datasets are preprocessed and the features are extracted. Then, the extracted features of the trained data's are given using deep belief network (DBN) classifier to get pure sentiments (positive or negative) with accuracy. Here, the performance metrics of the accuracy, recall, and precision, F‐measure, specificity, and sensitivity are calculated. The simulation process is executed in Python platform. The proposed chaotic coyote optimized deep belief network (CCO‐DBN) attains accuracy 9.8%, precision 17.2%, recall 5.61%, F‐measure 17.07%, specificity 2.247%, sensitivity 13.25% is higher than the existing methods such as FCM‐DFA, GA, SVM‐RFA.

Rotating Machinery Fault Diagnosis Using HHO-RSSD and RCGmvMAAPE

Rotating machinery has played an enormous role in industrial production, and its stable operation is related to whether production can proceed smoothly. At present, multichannel entropy-based methods are usually be adopted to analyze multichannel vibration signals. However, the collected signal may only have one channel in the actual situation. At this time, analyzing only a single channel signal cannot effectively utilize the advantages of multivariate analysis. For this reason, this paper presents a novel multivariate analysis approach and applies it to the fault diagnosis of machinery. Firstly, the parameter-optimized resonance sparse decomposition (RSSD) algorithm is adopted to decompose the single-channel vibration signal into high and low resonance components. Then, the two components are regarded as dual-channel vibration signals and input into the refined composite generalized multivariate multiscale amplitude aware permutation entropy (RCGmvMAAPE) method to gain fault features. Eventually, the features are input to the deep belief network (DBN) classifier to perform fault judgment. The experiments of rotating machinery are carried to verify the effectiveness of the developed approach. The results display that the proposed fault diagnosis method can achieve the classification accuracy of 100% and 98% when only a single-channel vibration signal is used, which is better than the fault diagnosis method based on a multichannel vibration signal and enjoys strong stability.

Taylor series‐based deep belief network for automatic classification of diabetic retinopathy using retinal fundus images

AbstractThe diagnosis of diabetic retinopathy (DR) disease in the early stage is very important to reduce the risk in DR treatment. Different methods are in practice for detecting the lesions automatically with the retinal image. However, detecting the occurrence of exudates in the macular region poses a challenging task in the computer‐assisted diagnosis of DR. A robust and computationally efficient model for localizing the lesions and features in the retinal fundus image is processed in this research by proposing the Taylor‐based deep belief network (T‐based DBN) classifier. Exudates and their contours are determined based on the blood vessel and optic disc segmentation model. The microaneurysms are detected based on the wavelet model, and the lesions are segmented with the thresholding and binarization approach. The proposed T‐based DBN is highly effective in classifying the DR, based on the multiple layers associated with the restricted Boltzmann machines (RBM) and multi‐layer perceptron (MLP) layer. The proposed T‐based DBN is the integration of the Taylor series with the DBN classifier. The proposed T‐based DBN produces an accurate detection rate and yields better theoretical error bounds. The performance revealed by the proposed model is evaluated using the metrics, namely specificity, sensitivity, and accuracy, with the values of 90.757%, 92.225%, and 92.122%, respectively.

Breast cancer detection in digital mammography using a novel hybrid approach of Salp Swarm and Cuckoo Search algorithm with deep belief network classifier

ABSTRACT The Computer Aided Diagnosis (CAD) system has evolved as a useful tool for radiologists to classify breast cancer images into various categories, enabling early diagnosis and treatment. In CAD model construction, feature selection is essential for determining a subset of appropriate features to diagnose breast cancer. Salp Swarm Algorithm (SSA) is an evolutionary algorithm that simulates the swarming behaviour of salps. SSA has some advantages such as simplicity, speed in searching and ease of hybridization with other optimization algorithms. However, it suffers from being stuck in local optima and having slow convergence. To address these issues, this work proposes a novel hybridization algorithm called SSACS by combining the SSA with Cuckoo Search (CS) to improve convergence and exploitation capabilities. Further, the Deep Belief Network (DBN) classifier is applied to classify the mammogram images and improve the diagnosis rates. The proposed system’s efficacy is validated with the benchmark database of the Mammographic Image Analysis Society (mini-MIAS) dataset. The experimental findings indicate that the proposed SSACS with DBN classifier outperforms the state-of-the-art methods.

Face Recognition Based on Fractal Code and Deep Belief Networks

An enhanced algorithm to recognize the human face using bi-dimensional fractal codes and deep belief networks is presented in this work. The proposed method is experimentally robust against variations in the appearance of human face images, despite different disturbances affecting the measurements and the acquisition process such as occlusion, changes in lighting, pose, and expression or the presence or absence of structural components. That is mainly based on fractal codes (IFS) and bi-dimensional subspaces for features extraction and space reduction, combined with a deep belief network (DBN) classifier. The evaluation is performed through comparisons using probabilistic neural network (PNN) and nearest neighbours (KNN) approaches on three well-known databases (FERET, ORL, and FEI). The results suggest the effectiveness and robustness of the proposed approach.

Improved Grasshopper Optimization Algorithm based Feature Selection with Evolutionary Outlay-Aware Deep Belief Network Classifier (IGOA-EOA-DBNC) for High Dimensional Datasets

Background: High dimensional datasets contain the curse of dimensionality, and hence data mining becomes a more difficult task. Feature selection in the knowledge data and discovery process provides a solution for this curse of dimensionality issue and helps the classification task reduce the time complexity and improve the accuracy. Objectives: This paper aims to recognize a bio-inspired algorithm that best suits feature selection and utilizes optimized feature selection techniques. This algorithm is used to design machine learning classifiers that are suitable for multiple datasets and for both high dimensional datasets, moreover to carry out performance analysis with regards to the accuracy of a classification and the processing time for classification. Methods: This study employs an improved form of grasshopper optimization algorithm to perform feature selection task. Evolutionary outlay aware deep belief network is used to perform the classification task. Findings: In this research, 20 UCI benchmark data sets are taken with full 60 features and 30000 instances. The datasets are Mammography, Monks-1, Bupa, Credit, Parkinson's, Monk-2, Sonar, Ecoli, Prognostic, Ionosphere, Monk-3, Yeast, Car, Blood, Pima, Spect, Vert, Prognostic, Contraceptive, and Tic-Tac-Toe endgame. Table 1 describes the dataset details, number of instances, datasets and features. The overall performance is performed using MATLAB 6.0 tool, which runs on Microsoft Windows 8, and the configuration is Core 13 processor with 1 TB hard disk and 8GB RAM. Performance standards, like classification accuracy and the processing time for classification, is achieved. Novelty: Interestingly, the Improved Grasshopper Optimization Algorithm uses error rate and classification accuracy of the Evolutionary Outlay Aware –Deep Belief Network Classifier as fitness function values. This combined work of classification and feature selection is briefly represented as IGOA-EOA-DBNC. Twenty datasets are selected for testing the performance regarding elapsed time and accuracy, which gives better results.