Deep Belief Network Model Research Articles

Determination and investigation of shear wave velocity based on one deep/machine learning technique

Rock physics plays an important role in the oil, gas, and water industries by providing essential data for reservoir management. This study focuses on predicting Shear Wave Velocity (Vs), a key geophysical parameter, using the Deep Belief Network (DBN) and Random Forest (RF) algorithms. The dataset is divided for training, validation, and testing, revealing that the DBN model surpasses the RF and empirical models in accuracy. With a low RMSE value of 0.0398 km/s in testing and an R2 of 0.9775, the DBN model demonstrates precision in Vs prediction. The analysis identifies specific parameters—Measure Depth (MD), Caliper (CALL), Gamma Ray (GR), Shallow Electrical Resistivity (RES-S), Medium Electrical Resistivity (RES-M), Deep Electrical Resistivity (RES-D), Compressional Wave Velocity (DCT), Neutron Porosity (NPHI), and Photoelectric Coefficient Index (PEF)—as significantly influencing Vs, while CALI and GR have a relatively lesser effect. The results of the Vs prediction indicated that the DBN algorithm excels in deep feature extraction, unsupervised pretraining, and efficient handling of complex data. Ultimately, the DBN algorithm proves to be a superior and time-saving alternative for Vs prediction and analysis in subsurface reservoirs.

Dynamic role of clean energy and sustainable economic growth in coastal region: Novel observations from China

China's coastal region is the major geographical unit for the future development of China's industrial sector. The transformation of basic structure to high-class development in China's coastal places is a significant tool for promoting the changes related to quality, power and efficiency in regional economic development. In the 21st century, environmental and energy issues have increased worldwide, and challenges related to environmental pollution, energy crises, and ecological imbalances have emerged. To climate change and energy utilization, the sustainable progress of clean energy is the new route of future energy development. Based on China's non-polluting energy growth process in the last ten years, this article explores China's clean/green energy policies and economic growth development plans. Clean energy utilization is crucial for sustainable development in the context of high-quality economic growth and climate change. However, the monetary evolution and carbon emission are not investigated whole from the clean energy aspects. Using Wind energy sources as the acceptable variable, this paper employs threshold regression and impulse functions to assess the energy consumption and economic growth on carbon emission in 30 Chinese provinces over the 2000 to 2020 period. The Deep Belief Network (DBN) model predicts wind energy utilization and efficiency. The results show that economic development and carbon emissions are connected. Further, growth influences promote the offset of carbon emissions. Green innovation alters the nexus of carbon emissions, and China's economy reduces carbon usage. It provides the decision-making policies for clean energy development.

Frequency prediction of a post-disturbance power system using a hybrid ARIMA and DBN model

The online prediction of power system dynamic frequency helps to guide the choice of control measures quickly and accurately after a disturbance, and this then ensures the reliable and stable operations of a power system. However, the prediction performance of the traditional single model is not accurate enough, and the prediction method cannot reflect the dynamic mechanism of the power system. To address these challenges, based on the analysis of the mechanism of the dynamic operation of a power system, a dynamic frequency online prediction method using the autoregressive integrated moving average (ARIMA) model and the deep belief network (DBN) is proposed in this paper. First, according to the mechanism of the dynamic operation of a power system, the dynamic frequency can be regarded as having two stages after the disturbance occurs. In the first stage, the frequency changes monotonously in the short term, which is predicted by the ARIMA model. Furthermore, the second stage is an oscillation phase with changing amplitude, which is predicted by the DBN. The calibration process is used to combine the two predicted results. Second, the three metrics including the frequency nadir (fnadir), the quasi-steady state frequency (fss), and the frequency curve obtained through the prediction are analyzed to measure the accuracy of the prediction results. Finally, to verify the accuracy of the proposed model, the IEEE 10-generator 39-bus benchmark system is used for verification.

Research on Classification Algorithm of Silicon Single-Crystal Growth Temperature Gradient Trend Based on Multi-Level Feature Fusion.

In the process of silicon single-crystal preparation, the timely identification and adjustment of abnormal conditions are crucial. Failure to promptly detect and resolve issues may result in a substandard silicon crystal product quality or even crystal pulling failure. Therefore, the early identification of abnormal furnace conditions is essential for ensuring the preparation of perfect silicon single crystals. Additionally, since the thermal field is the fundamental driving force for stable crystal growth and the primary assurance of crystal quality, this paper proposes a silicon single-crystal growth temperature gradient trend classification algorithm based on multi-level feature fusion. The aim is to accurately identify temperature gradient changes during silicon crystal growth, in order to promptly react to early growth failures and ensure the stable growth of high-quality silicon single crystals to meet industrial production requirements. The algorithm first divides the temperature gradient trend into reasonable categories based on expert knowledge and qualitative analysis methods. Then, it fuses the original features of actual production data, shallow features extracted based on statistical information, and deep features extracted through deep learning. During the fusion process, the algorithm considers the impact of different features on the target variable and calculates mutual information based on the difference between information entropy and conditional entropy, ultimately using mutual information for feature weighting. Subsequently, the fused multi-level feature vectors and their corresponding trend labels are input into a Deep Belief Network (DBN) model to capture process dynamics and classify trend changes. Finally, the experimental results demonstrate that the proposed algorithm can effectively predict the changing trend of thermal field temperature gradients. The introduction of this algorithm will help improve the accuracy of fault trend prediction in silicon single-crystal preparation, thereby minimizing product quality issues and production interruptions caused by abnormal conditions.

IoT-assisted Human Activity Recognition Using Bat Optimization Algorithm with Ensemble Voting Classifier for Disabled Persons

Internet of Things (IoT)-based human action recognition (HAR) has made a significant contribution to scientific studies. Furthermore, hand gesture recognition is a subsection of HAR, and plays a vital role in interacting with deaf people. It is the automatic detection of the actions of one or many subjects using a series of observations. Convolutional neural network structures are often utilized for finding human activities. With this intention, this study presents a new bat optimization algorithm with an ensemble voting classifier for human activity recognition (BOA-EVCHAR) technique to help disabled persons in the IoT environment. The BOA-EVCHAR technique makes use of the ensemble classification concept to recognize human activities proficiently in the IoT environment. In the presented BOA-EVCHAR approach, data preprocessing is generally achieved at the beginning level. For the identification and classification of human activities, an ensemble of two classifiers namely long short-term memory (LSTM) and deep belief network (DBN) models is utilized. Finally, the BOA is used to optimally select the hyperparameter values of the LSTM and DBN models. To elicit the enhanced performances of the BOA-EVCHAR technique, a series of experimentation analyses were performed. The extensive results of the BOA-EVCHAR technique show a superior value of 99.31% on the HAR process.

Prediction of surface roughness using deep learning and data augmentation

PurposeSurface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.Design/methodology/approachThis study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.FindingsThe adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.Originality/valueA roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

Sentiment Analysis on Twitter Using Deep Belief Network Optimized with Particle Swarm Optimization

Deep Belief Network is a type of artificial neural network that is widely used in machine learning and deep learning tasks that allows it to learn hierarchical representations of the input data. However, Deep Belief Network has a drawback of being sensitive to hyperparameters. DBN has several hyperparameters that need to be chosen appropriately for the network to function effectively. Poor hyperparameter choices can lead to unstable training or poor performance. Therefore, the Particle Swarm Optimization algorithm is used to search for the best hyperparameters, which can lead to stable training and improved performance. The purpose of this study is to analyze public sentiment on Twitter using the Deep Belief Network method and to optimize it using Particle Swarm Optimization. The evaluation results obtained in this study are 71.4% accuracy, 71.7% precision, 71.4% recall, and 71.2% F1-score in the Deep Belief Network model which is optimized by the Particle Swarm Optimization algorithm, whereas when compared with the Deep Belief Network model alone gets evaluation results of 68.3% Accuracy, 69.0% Precision, 68.3% Recall and 68.0% F1-score. These results indicate that the use of the Particle Swarm Optimization algorithm is quite influential in analyzing sentiment in text.

Deepfake image detection and classification model using Bayesian deep learning with coronavirus herd immunity optimizer

<p>Deepfake images are combined media constructed from deep learning (DL) methods, usually Generative Adversarial Networks (GANs), to manipulate visual content, often giving rise to convincing and fabricating descriptions of scenes or people. The Bayesian machine learning (ML) model has made crucial strides over the past two decades, illustrating promise in diverse applications. In deepfake images, detection utilizes computer vision (CV) and ML to spot manipulated content by analyzing unique artefacts and patterns. Recent techniques utilize DL to train neural networks to discriminate between real and fake images, improving the fight against digital manipulation and preserving media integrity. These systems can efficiently detect subtle inconsistencies or anomalies specific to deepfake creations by learning from large datasets of both real and deepfake images. This enables the mitigation of fraudulent content and reliable detection in digital media. We introduce a new Coronavirus Herd Immunity Optimizer with a Deep Learning-based Deepfake Image Detection and Classification (CHIODL-DIDC) technique. The CHIODL-DIDC technique aimed to detect and classify the existence of fake images. To accomplish this, the CHIODL-DIDC technique initially used a median filtering (MF) based image filtering approach. Besides, the CHIODL-DIDC technique utilized the MobileNetv2 model for extracting feature vectors. Moreover, the hyperparameter tuning of the MobileNetv2 model was accomplished using the CHIO method. For deepfake image detection, the CHIODL-DIDC technique implements the deep belief network (DBN) model. Finally, the Bayesian optimization algorithm (BOA) was utilized to select the effectual hyperparameter of the DBN model. The CHIODL-DIDC method's empirical analysis was examined using a benchmark fake image dataset. The performance validation of the CHIODL-DIDC technique illustrated a superior accuracy value of 98.16% over other models under $ Acc{u}_{y} $ , $ Pre{c}_{n} $ , $ Rec{a}_{l} $ , $ {F}_{Score} $ , and MCC metrics.</p>

Chicken swarm optimization modelling for cognitive radio networks using deep belief network-enabled spectrum sensing technique.

Cognitive radio networks (CRN) enable wireless devices to sense the radio spectrum, determine the frequency state channels, and reconfigure the communication variables to satisfy Quality of Service (QoS) needs by reducing energy utilization. In CRN, spectrum sensing is an essential process that is highly challenging and can be addressed by several traditional techniques, such as energy detection, match filtering, etc. For now, the current models' performance is impacted by the comparatively low Signal to Noise Ratio (SNR) of recognized signals and the insignificant quantity of traditional signal samples. This research proposals a new spectral sensing technique for cognitive radio networks (SST-CRN) that addresses the drawbacks of predictable energy detection models. With the use of a deep belief network (DBN), the suggested model contributes to accomplish a nonlinear threshold based on the chicken swarm algorithm (CSA). The proposed DBN enabled SST-CRN technique goes through two phases in a organized process: offline and online. Throughout the offline phase, the DBN model is methodically trained on pre-gathered data, developing the aptitude to identify problematic patterns and examples from the spectral features of the radio environment. This stage involves extensive feature extraction, validation, and model development to ensure that the DBN can professionally represent complicated spectral dynamics. Additionally, online spectrum sensing is conducted during the real communication phase to enable real-time adaptation to dynamic changes in the spectrum environment. Offline spectrum sensing is typically performed during a devoted sensing period before actual communication begins. When combined with DBN's deep learning capabilities and CSO's innate nature-inspired algorithms, a synergistic framework is created that enables CRNs to explore and allocate incidences on their own with astonishing accuracy. The proposed solution considerably improves the spectrum efficiency and resilience of CRNs by harnessing the power of DBN, which leads to more effective resource utilization and less interference. The Simulation results show that our proposed strategy produces more accurate spectrum occupancy assessments. The result parameters such as probability of detection, SNR of -24dB, the SST-CRN perfect has increased a developed Pd of 0.810, whereas the existing methods RMLSSCRN-100 and RMLSSCRN-300 have accomplished a lower Pd of 0.577 and 0.736, respectively. Our deep learning methodology uses convolutional neural networks to automatically learn and adapt to dynamic and complicated radio environments, improving accuracy and flexibility over classic spectrum sensing approaches. Future research might focus on improving CSO algorithms to better optimize the spectrum sensing process, enhancing the reliability of DBN-enabled sensing techniques.

Deep belief networks in understanding and predicting changes in students’ political attitudes

Abstract Leveraging data collection software, this study gathered and preprocessed text data reflecting students’ political attitudes across five dimensions: political cognition, ruling party cognition, positive political emotion, negative political emotion, and political behavioral tendency. The processed data were subsequently stored in Excel format. These dimensions of change in students’ political attitudes served as inputs for a deep belief network (DBN) model, with the changes in attitudes being the outputs. The model’s parameters were meticulously tuned to optimize its predictive capabilities, aimed at effectively constructing a prediction model for shifts in students’ political attitudes using the deep belief network. To refine the model’s parameters and minimize errors during inverse fine-tuning, the chimpanzee algorithm (ChOA) was employed. This novel approach, termed ChOA-DBN, facilitated the successful construction of the predictive model for students’ political attitude changes. The model’s efficacy was assessed using the collected data on students’ political attitudes. Notably, the DBN model optimized via the chimpanzee algorithm demonstrated robust predictive performance, particularly when processing sample points exhibiting significant changes in students’ political attitudes (ranging from 20 to 45). The model’s predictions predominantly fell within a ±15% error margin. This research not only showcases the accuracy of the ChOA-DBN model in predicting alterations in students’ political attitudes but also underscores its potential contribution to fostering the development of accurate political perspectives among students.

Design of a Digital Exhibition Service System Under the Deep Belief Network Models

Design of a Digital Exhibition Service System Under the Deep Belief Network Models

Enhanced Jaya Optimization Algorithm with Deep Learning Assisted Oral Cancer Diagnosis on IoT Healthcare Systems

Recently, healthcare systems integrate the power of deep learning (DL) models with the connectivity and data processing capabilities of the Internet of Things (IoT) to enhance the early recognition and diagnosis of disease. Oral cancer diagnosis comprises the detection of cancerous or pre-cancerous abrasions in the oral cavity. Timely identification is essential for successful treatment and enhanced prognosis. Here is an overview of the key aspects of oral cancer diagnosis. One potential benefit of utilizing DL for oral cancer detection is that it analyses huge counts of data fast and accurately, and it could not need clear programming of the rules for recognizing abnormalities. This can create the procedure of detecting oral cancer more effective and efficient. Thus, the study presents an Enhanced Jaya Optimization Algorithm with Deep Learning Based Oral Cancer Classification (EJOADL-OCC) method. The presented EJOADL-OCC method aims to classify and detect the existence of oral cancer accurately and effectively. To accomplish this, the presented EJOADL-OCC method initially exploits median filtering for the noise elimination. Next, the feature vector generation process is performed by the residual network (ResNetv2) model with EJOA as a hyperparameter optimizer. For accurate classification of oral cancer, a continuously restricted Boltzmann machine with a deep belief network (CRBM-DBN) model. The simulated validation of the EJOADL-OCC algorithm is tested by the series of simulations and the outcome demonstrates its supremacy over present DL approaches.

Evolutionary Algorithm with Deep Learning based Fall Detection on Internet of Things Environment

Falling is among the most threatening event proficient by the ageing population. There is a necessity for the development of the fall detection (FD) system with the increasing ageing population. FD in an Internet of Things (IoT) platform has developed as a vital application with the rapidly increasing population of aging population and the essential for continuous health monitoring. Falls among the ageing can performance in serious injuries, decreased independence, and longer recovery periods. The FD approach can constructed on deep learning (DL) approaches, especially, Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are capable in learning difficult patterns from the sensor data. The CNNs investigate the spatial features, but the RNNs approach the temporal dependencies, allowing accurate recognition of fall events. This study presents an Evolutionary Algorithm with Deep Learning based Fall Detection and Classification (EADL-FDC) methodology in the IoT platform. The projected EADL-FDC algorithm allows the DL approaches for the effective recognition and classification of falls for disabled and ageing people. In the presented EADL-FDC technique, the span-partial structure, and attention (SPA-Net) model is utilized for feature extraction purposes. In addition, the symbiotic organism search (SOS) approach was used for the parameter selection of the SPA-Net system. The deep belief network (DBN) model is applied to classify the fall events. Lastly, the moth flame optimization (MFO) algorithm can be utilized to finetune the hyperparameters related to the DBN algorithm. The stimulation analysis of the EADL-FDC method takes place on the fall detection dataset. The experimental outcome depicts the remarkable solution of the EADL-FDC technique over other existing DL methods.

Virtual machine placement in energy aware load balancer using fog classifier

Cloud datacenter carries huge volume of data and tasks which is allocating resources to multiple workstations. Most of the cloud services are operating service level agreement (SLA) placements. During execution datacenter emits carbon and makes the energy. So operation cost always consideration fact. We need to address this challenge by using energy aware load balancer. This load balancer can be fixed in Virtual Machines (VM) and Classifier is required for selecting VMs. Employing the VMs is very important factor so fog enabled services is required for distributed geo physical load balancer with energy efficiency. In this paper we propose offloading VM services and Fog classifier for load balancing the cloud services. Placing the VM from one host to another we use Host Load Balancer with Energy Aware placement algorithm. In this case dynamical cloud environment can be tested and compare the host results. This is empirical approach for place the VMs without compromising the users. The simulations are done by using CloudSim and TensorFlow is used of generating deep belief network model for preparing VM placement. Our proposed method achieves 96% energy efficiency with minimum migration cost. The results are compared with existing placement methods based on active host availability.

A brief study on rice diseases recognition and image classification: fusion deep belief network and S-particle swarm optimization algorithm

In the regions of southern Andhra Pradesh, rice brown spot, rice blast, and rice sheath blight have emerged as the most prevalent diseases. The goal of this research is to increase the precision and effectiveness of disease diagnosis by proposing a framework for the automated recognition and classification of rice diseases. Therefore, this work proposes a hybrid approach with multiple stages. Initially, the region of interest (ROI) is extracted from the dataset and test images. Then, the multiple features are extracted, such as color-moment-based features, grey-level cooccurrence matrix (GLCM)-based texture, and shape features. Then, the S-particle swarm optimization (SPSO) model selects the best features from the extracted features. Moreover, the deep belief network (DBN) model trained by SPSO is based on optimal features, which classify the different types of rice diseases. The SPSO algorithm also optimized the losses generated in the DBN model. The suggested model achieves a hit rate of 94.85% and an accuracy of 97.48% with the 10-fold cross-validation approach. The traditional machine learning (ML) model is significantly less accurate than the area under the receiver operating characteristic curve (AUC), which has an accuracy of 97.48%.

Knowledge Transfer-Based Sparse Deep Belief Network.

Deep learning has made remarkable achievements in various applications in recent years. With the increasing computing power and the "black box" problem of neural networks, however, the development of deep neural networks (DNNs) has entered a bottleneck period. This article proposes a novel deep belief network (DBN) based on knowledge transfer and optimization of the network structure. First, a neural-symbolic model is proposed to extract rules to describe the dynamic operation mechanism of the deep network. Second, knowledge fusion is proposed based on the merge and deletion of the extracted rules from the DBN model. Finally, a new DNN, knowledge transfer-based sparse DBN (KT-SDBN) is constructed to generate a sparse network without excessive information loss. In comparison with DBN, KT-SDBN has a more sparse network structure and better learning performance on the existing knowledge and data. The experimental results in the benchmark data indicate that KT-SDBN not only has effective feature learning performance with 30% of the original network parameters but also shows a large compression rate that is far larger than other structure optimization algorithms.

Innovation and Practice of Educational Management System in Colleges and Universities Based on the Concept of Data Analysis Education

Abstract This paper proposes the evaluation method of college education management mode based on deep belief network (DBN), establishes the evaluation index of college education management mode, takes it as the input of the deep belief network, adopts the divergence algorithm and backpropagation algorithm to learn and optimize the parameters of the deep belief network, and establishes the evaluation model of the education management mode of universities. The evaluation method of the college education management model evaluation of the deep belief network is verified by evaluating the results and performing error analysis on 438 questionnaire test samples. The results show that the number of training steps of the adopted DBN model in the supervised tuning stage is 214, and the error fluctuation range is ±0.6, which achieves the expected effect, indicating that the adoption of the model has accuracy in the evaluation of college education management model. Under the concept of data analysis education, the evaluation method proposed in this paper has a positive significance for the innovation and practice of educational and teaching management systems.

Chaotic tumbleweed optimization algorithm with stacked deep learning based cyberattack detection in industrial CPS environment

Cyberattacks on cyber-physical systems (CPS) have led to severe concerns, and then it is most significant to identify the attacks in the initial phase. But, there are major problems that are resolved in this area; it contains the capability of the security method for detecting earlier unknown attacks. These challenges are resolved with system behaviour analysis manners and semi-supervised or unsupervised machine learning (ML) approaches. The efficacy of the attack recognition method is strongly dependent upon the databases utilized for training the ML approaches. Anomaly detection is the procedure of recognizing anomalous procedures, which could not equal the predictable system behaviour. It permits the recognition of novel and secret attacks. Presently, anomaly detection systems are frequently executed utilizing ML like shallow (or traditional) learning and deep learning (DL). This article develops a Chaotic Tumbleweed Optimization Algorithm with Stacked Deep Learning based Cyberattack Detection (CTOASDL-CD) approach in the Industrial CPS platform. The CTOASDL-CD technique intends to exploit the FS with an optimal hyperparameter-tuned DL model for the detection of cyberattacks. To resolve the high dimensionality issue, the CTOASDL-CD technique uses CTOA for feature selection (FS) purposes. Besides, the stacked deep belief network (SDBN) model can be utilized for the recognition of cyberattacks. Finally, the tunicate swarm algorithm (TSA) has been deployed for the capable selection of hyperparameter values of the SDBN approach. To determine the improved performance of the CTOASDL-CD approach, a wide-ranging simulation value was performed. The experimental values demonstrated the capable outcome of the CTOASDL-CD methodology in accomplishing security in the industrial CPS environment.

Artificial algae optimizer with hybrid deep learning based yoga posture recognition model

Numerous people are interested in learning yoga due to the increased tension levels in the modern lifestyle, and there are a variety of techniques or resources available. Yoga is practiced in yoga centers, by personal instructors, and through books, the Internet, recorded videos, etc. As the aforementioned resources may not always be available, a large number of people will opt for self-study in fast-paced lifestyles. Self-learning makes it impossible to recognize an incorrect posture. Incorrect poses will have a negative effect on the patient’s health, causing severe agony and long-term chronic issues. Computer vision (CV)-related techniques derive pose features and conduct pose analysis using non-invasive CV methods. The application of machine learning (ML) and artificial intelligence (AI) techniques to an inter-disciplinary field like yoga becomes quite difficult. Due to its potent feature learning ability, deep learning (DL) has recently achieved an impressive level of performance in classifying yoga poses. In this paper, an artificial algae optimizer with hybrid deep learning-based yoga pose estimation (AAOHDL-YPE) model is presented. The presented AAOHDL-YPE model analyzes yoga video clips to estimate pose. Utilizing Part Confidence Map and Part Affinity Field with bipartite equivalent and parsing, OpenPose can be employed to determine the joint location. The deep belief network (DBN) model is then used for Yoga recognition. Finally, the AAO algorithm is utilized to enhance the EfficientNet model’s recognition performance. The results of a comprehensive experimentation analysis reveal that the AAOHDL-YPE technique produces superior results in comparison to existing methods.

Sentiment Analysis for Online Product Reviews and Recommendation Using Deep Learning Based Optimization Algorithm

Recently, online shopping is becoming a popular means for users to buy and consume with the advances in Internet technologies. Satisfaction of users could be efficiently improvised by carrying out a Sentiment Analysis (SA) of larger amount of user reviews on e-commerce platform. But still, it is a challenge to envision the precise sentiment polarity of the user reviews due to the modifications in sequence length, complicated logic, and textual order. In this study, we propose a Hybrid-Flash Butterfly Optimization with Deep Learning based Sentiment Analysis (HFBO-DLSA) for Online Product Reviews. The presented HFBO-DLSA technique mainly aims to determine the nature of sentiments based on online product reviews. For accomplishing this, the presented HFBO-DLSA technique applies data pre-processing at the preliminary stage to make it compatible. Besides, the HFBO-DLSA model uses deep belief network (DBN) model for classification. The HFBO algorithm is used as a hyperparameter tuning process to improve the SA performance of the DBN method. The experimental validation of the presented HFBO-DLSA method has been tested under a set of datasets. The experimental results reveal that the HFBO-DLSA approach surpasses recent techniques in terms of SA outcomes. Specifically, when compared to various existing models on the Canon dataset, the HFBO-DLSA technique achieves remarkable results with an accuracy of 97.66%, precision of 98.54%, recall of 94.64%, and an F-score of 96.43%. In comparative analysis, other approaches such as ACO, SVM, and NN exhibit poorer performance, while TextCNN, BiLSTM, and RCNN approaches yield slightly improved SA results.