Hybrid Support Vector Machine Research Articles

Hybrid Transfer Learning and Support Vector Machine Models for Asphalt Pavement Distress Classification

Pavement condition evaluation plays a crucial role in assisting with the management of the highway infrastructure. However, the current methods used for assessing pavement conditions are costly, time-consuming, and subjective. There is a growing need to automate these assessment tactics and leverage low-cost technologies to enable widespread deployment. This study aims to develop robust and highly accurate models for classifying asphalt pavement distresses using transfer learning (TL) techniques based on pretrained deep learning (DL) networks. This topic has gained considerable attention in the field since 2015 when DL became the mainstream choice for various computer vision tasks. While progress has been made in TL model development, challenges persist in areas of accuracy, repeatability, and training cost. To tackle these challenges, this study proposes hybrid models that combine DL networks with support vector machines (SVMs). Three strategies were evaluated: single DL models using transfer learning (TLDL), hybrid models combining DL and SVM (DL+SVM), and hybrid models combining TLDL and SVM (TLDL+SVM). The performance of each strategy was assessed using statistical metrics based on the confusion matrix. Results consistently showed that the TLDL+SVM strategy outperformed the other approaches in accuracy and F1 scores, regardless of the DL network type. On average, the hybrid models achieved an accuracy of 95%, surpassing the 80% accuracy of the best single model and the 55% accuracy for DL+SVM without TL. The results clearly indicate that employing transfer-learned models as feature extractors, in combination with SVM as the classifier, consistently achieves exceptional performance.

Sentiment Analysis of Short Texts Using SVMs and VSMs-Based Multiclass Semantic Classification

ABSTRACT In our approach, a hybrid machine learning model is proposed which uses Enhanced Vector Space Model (EVSM) along with Hybrid Support Vector Machine (HSVM) classifier. Initially the social media-based information is retrieved using Enhanced Vector Space Model (EVSM). EVSMs are employed in order to characterize the text content by mapping them into high-dimensional vector spaces, capturing the relationships between words and their contextual meanings. Rigorous feature selection methods are employed to designate texts for review, and a multiclass semantic classification algorithm, specifically the HSVM classifier, is utilized for categorization. Decision tree algorithm is used along with SVM to refine the selection process. To enhance sentiment analysis accuracy, sentiment dictionaries are not only presented but also extended through the expansion of Stanford’s GloVE tool. To enhance precision, the proposed work introduces weight-enhancing methods for processing renowned text weights. Sentiments are classified into positive, negative, and neutral categories. Notably, the achieved results demonstrate improved accuracy, attributed to the incorporation of an emotional sentiment enhancement factor for determining weights and leveraging sentiment dictionaries for word availability. The accuracy is obtained to be 92.78% with 91.33% positive sentiment rate and 97.32% negative sentiment rate.

Hybrid Support Vector Machine for Predicting Accuracy of Conflict Flows in Software Defined Networks

Software Defined Networking (SDN) is an infrastructure platform for delivering simplified and compliant services with flexible services. These are the means of centralized maintenance and adaptive functions. SDN is affected by various contention flows and causes network performance issues. In this case, we need to provide efficient solutions to handle conflicting flows with better priority and actions. In this paper, we propose a DeepQ Residue method for analyzing normal and conflicting flow scenarios in the load balancing phase. During simulation, an open SDN network is generated using TensorFlow. We use a Hybrid Support Vector machine with an improved decision tree method to predict accuracy and performance. In this case, we analyze threads from 1000 to 100000 in increments of 10000 threads in each iteration. Here, we train a deep belief network with a decision-free feature for environmental simulation. Based on the simulation results, the accuracy of our proposed method reaches 97%, and we compare the results with the results of various existing methods. Our proposed algorithm provides a high-performance SDN application with different conflicting load-balanced flows.

Application of SVM model based on collaborative filtering hybrid algorithm in e-commerce recommendation

ABSTRACT The current e-commerce suggestion suffers from information overload, which makes it harder for customers to choose products. Due to this, researchers created a hybrid collaborative filtering algorithm and support vector machine e-commerce recommendation model to improve user recommendations. The support vector machine for effective offline classification also explains how to pick the right label threshold, the sparsity of the dataset, and other challenges from both the user- and item-oriented perspectives. According to the experimental results, the support vector machine classification methods based on user perspective and items from the Movielens dataset had average accuracies of 0.91 and 0.88, respectively, while the corresponding average accuracies on the Jester-joke dataset were 0.74 and 0.75. Both the user- and item-based collaborative filtering algorithms on the popular goods dataset generated noticeably fewer mean absolute errors than the cold goods dataset. The average coverage and accuracy of recommendations made using an e-commerce recommendation model that incorporates support vector machines and collaborative filtering algorithms are 90.2%, 92.8%, 88.4%, 83.7%, and 80.2%, respectively. This showed that the research methodology produces positive results in e-commerce recommendation.

A-Novel in this Technique Analysis Tuberculosis Patient Using Hybrid CNN and SVM Model

Tuberculosis (TB) remains a global health challenge with a significant impact on public health worldwide. This study addresses the prevalence and detection of T.B., focusing on India, a country experiencing rapid economic growth and notable transitions in various sectors. Despite advancements in healthcare delivery, communicable and non-communicable diseases, including TB, continue to pose substantial threats to health security. The proposed method for T.B. analysis integrates key modules, encompassing data collection, preprocessing, feature extraction, image splitting, classification, and performance estimation. Utilizing machine learning algorithms such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN), the study aims to enhance accuracy and efficiency in T.B. patient detection. The dataset includes chest X-rays from diverse sources, aiming to provide a comprehensive understanding of T.B. patterns. The study emphasizes the interconnected challenges of malnutrition and T.B., highlighting the importance of nutritional status assessment in public health. The linkage between undernutrition and infectious diseases, including TB, underscores the need for early detection and intervention. The proposed method offers a systematic approach to evaluating and addressing nutritional deficiencies in patients with chronic illnesses, contributing to improved clinical outcomes.

DEVELOPMENT OF HEALTH INSURANCE CLAIM PREDICTION METHOD BASED ON SUPPORT VECTOR MACHINE AND BAT ALGORITHM

Health insurance industry is very much needed by the community in handling the financial risks in the health sector. The number of claims greatly affects the achievement of profits and the sustainability of the health insurance industry. Therefore, filing claims by insurance users from year to year is important to be predicted in insurance firm. The Machine Learning (ML) method promises to be a good solution for predicting health insurance claims compared to conventional data analytics methods. Support Vector Machine (SVM) is one of the superior ML approaches. Nonetheless, SVM performance is controlled by the suitable selection of SVM parameters. The SVM parameters is typically selected by trial and error, sometimes resulting in not optimal performance and taking a long time to complete. Swarm intelligence-based algorithms can be used to select the best parameters from SVM. This method is capable of locating the global best solution, is simple to implemented, and doesn't involve derivatives. One of the best swarm intelligence algorithms is the Bat Algorithm (BA). BA has a faster convergence rate than other algorithms, for example Particle Swarm Optimization (PSO). Based on this situation, this paper offers the new classification model for predicting health insurance claim based on SVM and BA. The metrics utilized for evaluation are accuracy, recall, precision, f1-score, and computing time. The experimental outcomes show that the proposed approach is superior to the conventional SVM and the hybrid of SVM and PSO in forecasting health insurance claims. In addition, the proposed method has a substantially shorter computing time than the hybrid of SVM and PSO. The outcomes of the experiments also indicate that the new classification model for predicting health insurance claim based on the SVM and BA can avoid over-fitting condition.

A Machine Learning Model for Determination of Gender Utilizing Hybrid Classifiers

One part of forensic anthropology involves investigating skeletal remains to identify corpses, and many of these remains were found incomplete, burned, broken, or destroyed, making investigation challenging. This study aims to use the pelvis and femur to identify the gender of skeletal remains. The pelvis and femur have previously been proven to be accurate indicators of a corpse's gender. The identification process is done through the measurement of the subpubic angle of the pelvis and the angle taken straight down from the top of the femur to the patella and then straight up. The two measurements were combined using the principal component analysis (PCA) method into two attributes on the x and y axes. These attributes were later used as data for the machine learning model design. The design process consisted of an Artificial Neutral Network (ANN) design model and Support Vector Machine (SVM) design model combined into a hybrid machine learning system. The ANN and SVM hybrid machine learning were tested with acquired data. The result of the test using the confusion matrix showed 83.33% accuracy, which is categorized as "good classification" based on Area Under the Curve (AUC).

Novel comparative methodology of hybrid support vector machine with meta-heuristic algorithms to develop an integrated candlestick technical analysis model

PurposeThe proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).Design/methodology/approachIn addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.FindingsResults have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.Research limitations/implicationsIn this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.Originality/valueIn this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

Rapid Detection of Total Viable Count in Intact Beef Dishes Based on NIR Hyperspectral Hybrid Model

The total viable count (TVC) of bacteria is an important index to evaluate the freshness and safety of dishes. To improve the accuracy and robustness of spectroscopic detection of total viable bacteria count in a complex system, a new method based on a near-infrared (NIR) hyperspectral hybrid model and Support Vector Machine (SVM) algorithms was developed to directly determine the total viable count in intact beef dish samples in this study. Diffuse reflectance data of intact and crushed samples were tested by NIR hyperspectral and processed using Multiplicative Scattering Correction (MSC) and Competitive Adaptive Reweighted Sampling (CARS). Kennard–Stone (KS) and Samples Set Partitioning Based on Joint X-Y Distance (SPXY) algorithms were used to select the optimal number of standard samples transferred by the model combined with root mean square error. The crushed samples were transferred into the complete samples prediction model through the Direct Standardization (DS) algorithm. The spectral hybrid model of crushed samples and full samples was established. The results showed that the Determination Coefficient of Calibration (RP2) value of the total samples prediction set increased from 0.5088 to 0.8068, and the value of the Root Mean Square Error of Prediction (RMSEP) decreased from 0.2454 to 0.1691 log10 CFU/g. After establishing the hybrid model, the RMSEP value decreased by 9.23% more than before, and the values of Relative Percent Deviation (RPD) and Reaction Error Relation (RER) increased by 12.12% and 10.09, respectively. The results of this study showed that TVC instewed beef samples can be non-destructively determined based on the DS model transfer method combined with the hybrid model strategy. This study provided a reference for solving the problem of poor accuracy and reliability of prediction models in heterogeneous samples.

A novel hybrid adaptive framework for support vector machine-based reliability analysis: A comparative study

This study presents an innovative hybrid Adaptive Support Vector Machine - Monte Carlo Simulation (ASVM-MCS) framework for reliability analysis in complex engineering structures. These structures often involve highly nonlinear implicit functions, making traditional gradient-based first or second order reliability algorithms and Monte Carlo Simulation (MCS) time-consuming. The application of surrogate models has proven effective in addressing computational challenges associated with a large number of simulations. Support Vector Machine (SVM), as an emerging machine learning method suitable for small-sample scenarios, offers a well-established theoretical foundation and presents an effective model substitution approach for reliability analysis in engineering structures. However, the existing literature lacks a comprehensive and thorough comparative analysis of SVM's hybrid adaptive modeling approach, encompassing initial sampling methods and learning functions, with regards to both computational efficiency and accuracy. Additionally, there is a gap in adaptive modeling methods capable of accommodating diverse types of input uncertainty, the nonlinearity of limit state functions, and various application scenarios. In response to these gaps, this article introduces the ASVM-MCS framework, which addresses these challenges by considering different types of input variables and various failure modes. Moreover, this study provides a comprehensive evaluation of the ASVM-MCS framework's performance, including its initial sampling methods and learning functions, across a range of application scenarios, such as scenarios involving only random variables, mixed variables, and the reliability of series-parallel systems.

A hybrid SVM and kernel function-based sparse representation classification for automated epilepsy detection in EEG signals

Automatic epilepsy detection based on electroencephalography (EEG) is crucial for advancing the diagnosis and treatment of epilepsy. In this paper, we propose a novel classification algorithm called SVM-KSRC, which differs from integrated learning approaches. The algorithm establishes a connection between support vector machine (SVM) and kernel sparse representation classification (KSRC) using support vectors. Specifically, we extract two types of features from the pre-processed EEG signals in this study. During the training phase, these features are utilized to train the SVM model and construct the kernel sparse representation dictionary. We differentiate the SVM part of the features of the test data to determine whether SVM or KSRC should be employed for classifying the test data. Our method is evaluated on two publicly available datasets: University of Bonn dataset and Neurology and Sleep Centre-New Delhi dataset. Through 10 times 10-fold cross validation, our method demonstrates superior performance in epilepsy detection when compared to existing machine learning methods. The experimental results demonstrate that SVM-KSRC is more effective compared to SVM and KSRC used separately. It achieves over 99% accuracy in all binary classification tasks and attains 100% accuracy in certain tasks. The source code is publicly available at https://github.com/Walkeraaa/SVM-KSRC.

Performance enhancement in clustering cooperative spectrum sensing for cognitive radio network using metaheuristic algorithm

Spectrum sensing describes, whether the spectrum is occupied or empty. Main objective of cognitive radio network (CRN) is to increase probability of detection (Pd) and reduce probability of error (Pe) for energy consumption. To reduce energy consumption, probability of detection should be increased. In cooperative spectrum sensing (CSS), all secondary users (SU) transmit their data to fusion center (FC) for final measurement according to the status of primary user (PU). Cluster should be used to overcome this problem and improve performance. In the clustering technique, all SUs are grouped into clusters on the basis of their similarity. In cluster technique, SU transfers their data to cluster head (CH) and CH transfers their combined data to FC. This paper proposes the detection performance optimization of CRN with a machine learning-based metaheuristic algorithm using clustering CSS technique. This article presents a hybrid support vector machine (SVM) and Red Deer Algorithm (RDA) algorithm named Hybrid SVM–RDA to identify spectrum gaps. Algorithm proposed in this work outperforms the computational complexity, an issue reported with various conventional cluster techniques. The proposed algorithm increases the probability of detection (up to 99%) and decreases the probability of error (up to 1%) at different parameters.

Intelligent solubility estimation of gaseous hydrocarbons in ionic liquids

The research focuses on evaluating how well new solvents attract light hydrocarbons, such as propane, methane, and ethane, in natural gas sweetening units. It is important to accurately determine the solubility of hydrocarbons in these solvents to effectively manage the sweetening process. To address this challenge, the study proposes using advanced empirical models based on artificial intelligence techniques like Multi-Layer Artificial Neural Network (ML-ANN), Support Vector Machines (SVM), and Least Square Support Vector Machine (LSSVM). The parameters for the SVM and LSSVM models are estimated using optimization methods like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Shuffled Complex Evolution (SCE). Data on the solubility of propane, methane, and ethane in various ionic liquids are collected from reliable literature sources to create a comprehensive database. The proposed artificial intelligence models show great accuracy in predicting hydrocarbon solubility in ionic liquids. Among these, the hybrid SVM models perform exceptionally well, with the PSO-SVM hybrid model being particularly efficient computationally. To ensure a comprehensive analysis, different examples of hydrocarbons and their order are included. Additionally, a comparative analysis is conducted to compare the AI models with the thermodynamic COSMO-RS model for solubility analysis. The results demonstrate the superiority of the AI models, as they outperform traditional thermodynamic models across a wide range of data. In conclusion, this study introduces advanced artificial intelligence algorithms such as ML-ANN, SVM, and LSSVM in accurately estimating the solubility of hydrocarbons in ionic liquids. The incorporation of optimization techniques and variations in hydrocarbon examples improves the accuracy, precision, and reliability of these intelligent models. These findings highlight the significant potential of AI-based approaches in solubility analysis and emphasize their superiority over traditional thermodynamic models.

A Hybrid ROS-SVM Model for Detecting Target Multiple Drug Types

Misleading in determining the decision to use the target drug will be fatal, even to death. This study examines five pharmacological targets designated as types A, B, C, X, and Y. Early detection of misleading drug targeting will reduce the risk of death. This study aims to develop hybrid random oversampling techniques (ROS) and support vector machine (SVM) methods. The use of the oversampling technique in this study aims to balance classes in the dataset; due to the data collection in each class, there is a relatively large gap. This study applies five schemes to see which combination of models produces the highest accuracy. This study also uses five types of SVM kernels, linear, polynomial, gaussian, RBF, and sigmoid, combined with the ROS oversampling technique. Our proposed model combines the ROS oversampling technique with a linear SVM kernel. We evaluated the proposed model and resulted in an accuracy of 97% and compared it with several experiments, including the ROS technique with a sigmoid kernel which only resulted in 50% accuracy. It can be seen from the results obtained that the linear kernel is very adaptive to data types in the form of numeric and nominal compared to other kernels. The method proposed in this study can be applied to other medical problems. Future research can be carried out using a combination of other sampling techniques with deep learning-based methods on this issue.

Hybrid Model Transfer Learning ResNet50 and Support Vector Machine for Face Mask Detection

The Covid-19 virus caused a health crisis in Indonesia. This virus is so deadly that it has caused many fatalities which have caused the whole world including the government to pay major attention to the Covid-19 pandemic. The Indonesian government has issued several policies to prevent the spread of this epidemic, one of which is wearing a mask in public places. One approach that is widely used in the field of computer vision is the Convolutional Neural Network (CNN) transfer learning. In this study, Hybrid Model Transfer Learning ResNet50 and SVM with RGB to HSV preprocessing is presented to detect masks in facial images. This model consists of three process components. The first is preprocessing RGB images to HSV, the second component is for Feature Extraction with ResNet50 and the third is mask classification on face images with Support Vector Machine (SVM). From dataset of 7328 training and testing data were carried out. The first model, without preprocessing the image data with ResNet50, produces an accuracy of 86.52%. The second model, the model with preprocessing converts image data from RGB to HSV with ResNet50 resulting in an accuracy of 99.18%. In the third model, without preprocessing with ResNet50 and SVM which has an accuracy of 90.55%. The fourth model, the model with preprocessing converts image data from RGB to HSV with ResNet50 and SVM resulting in an accuracy of 98.36%.

Predicting aqueous sorption of organic pollutants on microplastics with machine learning

Microplastics (MPs) are ubiquitously distributed in freshwater systems and they can determine the environmental fate of organic pollutants (OPs) via sorption interaction. However, the diverse physicochemical properties of MPs and the wide range of OP species make a deeper understanding of sorption mechanisms challenging. Traditional isotherm-based sorption models are limited in their universality since they normally only consider the nature and characteristics of either sorbents or sorbates individually. Therefore, only specific equilibrium concentrations or specific sorption isotherms can be used to predict sorption. To systematically evaluate and predict OP sorption under the influence of both MPs and OPs properties, we collected 475 sorption data from peer-reviewed publications and developed a poly-parameter-linear-free-energy-relationship-embedded machine learning method to analyze the collected sorption datasets. Models of different algorithms were compared, and the genetic algorithm and support vector machine hybrid model displayed the best prediction performance (R2 of 0.93 and root-mean-square-error of 0.07). Finally, comparison results of three feature importance analysis tools (forward step wise method, Shapley method, and global sensitivity analysis) showed that chemical properties of MPs, excess molar refraction, and hydrogen-bonding interaction of OPs contribute the most to sorption, reflecting the dominant sorption mechanisms of hydrophobic partitioning, hydrogen bond formation, and π-π interaction, respectively. This study presents a novel sorbate-sorbent-based ML model with a wide applicability to expand our capacity in understanding the complicated process and mechanism of OP sorption on MPs.

Comparative Study of Herbal Leaves Classification using Hybrid of GLCM-SVM and GLCM-CNN

Indonesia is a tropical country with a diverse range of plants that ancient people used for traditional medicines. However, the similarity in shape of the leaves became an obstacle to distinguishing them. Therefore, technological advancements are expected to help identify the herbal leaves to use them right on target according to their efficacy. In this research, image classification of katuk (Sauropus Androgynus) and kelor (Moringa Oleifera) leaves is applied using 3 different algorithms i.e hybrid of Gray Level Co-Occurrence Matrix (GLCM) feature extraction and Support Vector Machine (SVM) implementing 4 kernels namely linear, RBF, polynomial, and sigmoid; hybrid of GLCM and Convolutional Neural Network (CNN); and pure CNN. A dataset of 480 images has been collected with 2 different scenarios, including bright and dark intensities. Based on the result, a hybrid of GLCM and SVM showed the highest accuracy of 96% in the dark intensity test using a linear kernel, while sigmoid obtained the lowest accuracy of 35%. On the other hand, it has been discovered that CNN obtained the highest performance in the bright intensity test with an accuracy of 98%. While in the dark intensity test, a hybrid of GLCM and CNN is superior, obtaining 96% accuracy. In conclusion, CNN is more powerful for image classification with bright intensity. For dark intensity images, both the hybrid of GLCM+SVM (linear) and the hybrid of GLCM+CNN are fairly recommended.

Stability prediction of hard rock pillar using support vector machine optimized by three metaheuristic algorithms

Hard rock pillar is one of the important structures in engineering design and excavation in underground mines. Accurate and convenient prediction of pillar stability is of great significance for underground space safety. This paper aims to develop hybrid support vector machine (SVM) models improved by three metaheuristic algorithms known as grey wolf optimizer (GWO), whale optimization algorithm (WOA) and sparrow search algorithm (SSA) for predicting the hard rock pillar stability. An integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM models. Five parameters including pillar height, pillar width, ratio of pillar width to height, uniaxial compressive strength and pillar stress were set as input parameters. Two global indices, three local indices and the receiver operating characteristic (ROC) curve with the area under the ROC curve (AUC) were utilized to evaluate all hybrid models’ performance. The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices. Nevertheless, the performance of the SSA-SVM model for predicting the unstable pillar (AUC: 0.899) is not as good as those for stable (AUC: 0.975) and failed pillars (AUC: 0.990). To verify the effectiveness of the proposed models, 5 field cases were investigated in a metal mine and other 5 cases were collected from several published works. The validation results indicated that the SSA-SVM model obtained a considerable accuracy, which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability.

Effective foundation damping prediction of monopile-supported offshore wind turbines based on integrated fitting equation and PSO–SVM algorithm

Foundation damping affects the fatigue life of offshore wind turbines. However, the effective damping ratio determined using a finite element analysis is not always consistent with the input. A series of simplified finite element models were established using realistic structural properties for a monopile-supported wind turbine to analyze the influence of wall thickness, pile embedment length, structure density, and input damping ratio on the calculated effective damping ratio. An empirical fitting equation was proposed using a power function based on the comprehensive laws between various factors and the effective value. An integrated prediction approach combining this equation with a hybrid support vector machine and particle swarm optimization algorithm was subsequently described and its results compared with those of other intelligent algorithms. The results showed that the effective damping ratio was much smaller than the input value, with the former reaching a maximum of only 15% of the latter. The integrated prediction approach was shown to strike an equilibrium between computing efficiency and forecast accuracy. Considering the soil–structure interaction and elastoplastic behavior of soil only increased the effective damping ratio to 35% of the input value, and also obfuscated the roles of the various factors in the effective damping.

HRFSVM: identification of fish disease using hybrid Random Forest and Support Vector Machine.

Aquaculture fish diseases pose a serious threat to the security of food supplies. Fish species vary widely, and because they resemble one another so much, it is challenging to distinguish between them based solely on appearance. To stop the spread of disease, it is important to identify sick fish as soon as possible. Due to a lack of necessary infrastructure, it is still difficult to identify infected fish in aquaculture at an early stage. It is essential to promptly identify sick fish to stop the spread of disease. The purpose of this work is to suggest a machine learning technique based on the DCNN method for identifying and categorizing fish diseases. To solve problems involving global optimization, this paper suggests a brand-new hybrid algorithm called the Whale Optimization Algorithm with Genetic Algorithm (WOA-GA) and Ant Colony Optimization. In this work, for classification, the hybrid Random Forest algorithm is used. To facilitate the increased quality, distinctions between both the proposed WOA-GA-based DCNN architecture and the presently used methods for machine learning have been made. The effectiveness of the proposed detection technique is done with MATLAB. Performance metrics like sensitivity, specificity, accuracy, precision, recall, F-measure, NPV, FPR, FNR, and MCC are compared to the performance of the proposed technique.