Deep Belief Network Model Research Articles

Characterization of selected commercially available grilled lamb shashliks based on flavor profiles using GC-MS, GC × GC-TOF-MS, GC-IMS, E-nose and E-tongue combined with chemometrics

HS-SPME-GC-MS, SPME-Arrow-GC × GC-TOF-MS, HS-GC-IMS, Electronic-nose, and Electronic-tongue systems were applied in a feasibility study of the flavor characterization of five commercially available Chinese grilled lamb shashliks. A total of 198 volatile organic compounds (VOCs) were identified (∼71% by GC × GC-TOF-MS). Using data fusion strategies, five predictive models were applied to the composition of VOCs and brand identification of the lamb shashliks. Compared with partial least squares regression, support vector machine, deep neural network, and RegBoost modeling, a momentum deep belief network model performed best in predicting VOCs content and identifying shashlik brands (R2 above 0.96, and RMSE below 0.1). Intelligent sensory technology combined with chemometrics is a promising approach to the flavor characterization of shashliks and other food matrices.

The Virtual Data-sampling Method Based on Optimal DBN for High-Scale Distributed Photovoltaic Power Generation

In a distribution power grid with a high proportion of distributed photovoltaic (DP) connections, the real-time acquisition of the distributed photovoltaic power generation data is a costly and heavy task. In this paper, a virtual data sampling method based on Deep Belief Network (DBN) combined with a Second-order oscillating-firefly algorithm (SOO-FA) is proposed. It enables virtual data sampling from all distributed PV plants in a region where only one distributed PV plant has a complete data-sampling device and the other distributed PV plants have less expensive current sampling terminals. The firefly algorithm is utilized to continuously adjust and optimize the weights of the deep belief network, and the optimal DBN model is used to carry out the virtual collection of the power output data of the distributed PV equipment within the region. Finally, the proposed method has high accuracy for the PV output power by 100 distributed PV devices in the regional range in the simulation section.

The boundedness of functional differential equations of hospital financial software under information

Abstract In order to be efficient and accurate monitoring and early warning hospital financial software capabilities of hospitals, the author presents the working model of the different operations of the solutions should be used for financial forecasting in hospitals, in order to provide useful information to decision makers. Those. By the equality of the measure of suspension of the function of the difference between the difference and the difference between the difference in some cases, and by the use of the limits of the Solving the problem of the difference in the difference between the difference, the limit of the solution of the type of delay measure the difference. yes, and then the deep belief network is studied with the data set taken to measure to get the effects of the network; Finally, the deep belief network model is used for financial forecasting, which is compared with other methods in machine learning. The results show that with the increase of the number of network layers, the recognition accuracy is also improved. However, the number of network layers is not optimal. When the number of network layers is 3, the recognition performance is the best, which shows that the number of hidden layers in the deep trust network needs special analysis in combination with specific applications and practical information, in order to obtain the best number of network layers. It is clear that the deep belief network model has the best data fitting performance in terms of root mean square error and goodness of fit.

Research on the Uncertainty of Landslide Susceptibility Prediction Using Various Data-Driven Models and Attribute Interval Division

Two significant uncertainties that are crucial for landslide susceptibility prediction modeling are attribute interval numbers (AIN) division of continuous landslide impact factors in frequency ratio analysis and various susceptibility prediction models. Five continuous landslide impact factor interval attribute classifications (4, 8, 12, 16, 20) and three data-driven models (deep belief networks (DBN), random forest (RF), and neural network (back propagation (BP)) were used for a total of fifteen different scenarios of landslide susceptibility prediction studies in order to investigate the effects of these two factors on modeling and perform a landslide susceptibility index uncertainty analysis (including precision evaluation and statistical law). The findings indicate that: (1) The results demonstrate that for the same model, as the interval attribute value rises from 4 to 8 and finally to 20, the forecast accuracy of landslide susceptibility initially increases gradually, then progressively grows until stable. (2) The DBN model, followed by the RF and BP models, provides the highest prediction accuracy for the same interval attribute value. (3) AIN = 20 and DBN models have the highest prediction accuracy under 15 combined conditions, while AIN = 4 and BP models have the lowest. The accuracy and efficiency of landslide susceptibility modeling are higher when the AIN = 8 and DBN models are combined. (4) The landslide susceptibility index uncertainty predicted by the deeper learning model and the bigger interval attribute value is comparatively low, which is more in line with the real landslide probability distribution features. The conditions that the environmental component attribute interval is divided into eight parts and DBN models are used allow for the efficient and accurate construction of the landslide susceptibility prediction model.

Music Feature Recognition and Classification Using a Deep Learning Algorithm

This paper studied music feature recognition and classification. First, the common signal features were analyzed, and the signal pre-processing method was introduced. Then, the Mel–Phon coefficient (MPC) was proposed as a feature for subsequent recognition and classification. The deep belief network (DBN) model was applied and improved by the gray wolf optimization (GWO) algorithm to get the GWO–DBN model. The experiments were conducted on GTZAN and free music archive (FMA) datasets. It was found that the best hidden-layer structure of DBN was 1440-960-480-300. Compared with machine learning methods such as decision trees, the DBN model had better classification performance in recognizing and classifying music types. The classification accuracy of the GWO–DBN model reached 75.67%. The experimental results demonstrate the reliability of the GWO–DBN model. The GWO–DBN model can be further promoted and applied in actual music research.

An Efficient Investigation on Age-Related Macular Degeneration Using Deep Learning with Cloud-Based Teleophthalmology Architecture

AMD, or age-related macular degeneration, is the fourth most common visual ailment leading to blindness worldwide and mostly affects persons over the age of 60. Early-stage blindness may be reduced with timely and precise screening. High-resolution analysis and identification of the retinal layers damaged by illness is made possible by optical coherence tomography (OCT), a diagnostic technique. Setting up a comprehensive eye screening system to identify AMD is a difficult task. Manually sifting through OCT pictures for anomalies is a time-consuming and error-prone operation. Automatic feature extraction from OCT images may speed up the diagnostic process and reduce the potential for human mistake. Historically, several methods have been developed to identify characteristics in OCT pictures. This thesis documents the development and evaluation of many such algorithms for the identification of AMD. In order to minimize the severity of AMD, retinal fundus images must be employed for early detection and classification. In this work, we develop a useful deep learning cloud-based AMD categorization model for wearables. The suggested model is DLCTO-AMDC model, a patient outfitted with a head-mounted camera (OphthoAI IoMT headset) may send retinaldehyde fundus imageries to a secure virtual server for analysis. The suggested AMD classification model employs Inception v3 as the feature extractor and a noise reduction approach based on midway point filtering (MPF). The deep belief network (DBN) model is also used to detect and classify AMD. Then, an AOA-inspired hyperparameter optimisation method is used to fine-tune the DBN parameters. To ensure the DLCTO-AMDC model would provide superior classification results, extensive simulations were done using the benchmark dataset. The findings prove the DLCTO-AMDC model is superior to other approaches already in use.

English

Wireless capsule endoscopy (WCE) is a non-invasive wireless imaging technology that gained wider popularity. The main drawback of WCE is that it produces a massive amount of images that healthcare professionals should analyze, which is time-consuming. Many researchers have suggested machine learning and image-processing methods for classifying gastrointestinal tract disorders. Data augmentation and classical image processing techniques are integrated with the adjustable pre-trained deep convolutional neural network (DCNN) to categorize diseases in the digestive tract from WCE images. This study develops an Intelligent Wireless Endoscopic Image Classification using Gannet Optimization Algorithm with Deep Learning (IWEIC-GOADL) model. The IWEIC-GOADL technique mainly examines the WCE images for classification purposes. As a preprocessing step, the presented IWEIC-GOADL technique executes the Gabor filtering (GF) method for the noise removal process. In addition, the presented IWEIC-GOADL technique employs a deconvolution VGG19 (DeVGG19) model for feature vector generation, and its hyperparameter tuning process takes place by the GOA. Finally, the IWEIC-GODL technique applies the deep belief network (DBN) model for WCE image classification purposes. A wide range of simulations was performed on a benchmark dataset to demonstrate the better performance of the IWEIC-GODL technique. The stimulation outcome stated the improvements of the IWEIC-GODL algorithm over other recent techniques.

Knowledge-extracted deep learning diagnosis and its cloud-based management for multiple faults of chiller

Machine learning has the inspiring potential for fault prediction in HVAC systems, which is essential for the system energy efficiency. For the scenario of multiple faults, however, machine learning model is not efficient because the training datasets of this scenario are difficult to collect in the real applications. This paper proposes the novel knowledge-embedded deep belief network (DBN) method to diagnose the electronic-thermal and thermal-thermal multiple faults for chillers in the buildings. Firstly, the characteristics of electronic-thermal and thermal-thermal faults are analyzed through the experiments. Through detecting the sensor biases, the sensor-thermal faults are decoupled successfully. Secondly, the representative features are extracted using the proposed DBN model. In the fine-tuning process, the original DBN network is optimized through embedding the extracted knowledge rules. We integrate the knowledge-embedded DBN, extreme learning machine (ELM) and k-nearest neighbor (KNN) as the diagnosis model. The experimental datasets are used to validate the proposed multi-fault diagnosis method. The results show that its diagnosis performance is satisfactory when the training dataset of some fault is absent. Finally, we develop the cloud-based diagnosis and online management platform. The proposed method is deployed on the cloud to realize online diagnosis and smart management.

WASSERSTEIN GAN-GRADIENT PENALTY WITH DEEP TRANSFER LEARNING FOR 3D MRI ALZHEIMER DISEASE CLASSIFICATION

MRI scans for Alzheimer's disease (AD) detection are popular. Recent computer vision (CV) and deep learning (DL) models help construct effective computer assisted diagnosis (CAD) models for AD detection and categorization. Due to their dependence on huge training datasets and effective hyper parameter tuning procedures, most models failed. Transfer learning adjusts the final fully linked layers to use trained DL models on smaller datasets. It handles picture categorization problems well. This research introduces a Wasserstein GAN-Gradient Penalty with Deep Transfer Learning (WGANGP-DTL)-based AD classification model on 3D MRI data. WGANGP-DTL aims to accurately identify and classify AD. WGANGP increases dataset size. The WGANGP-DTL model pre-processes MRI images using image enhancement and segments them using 3DSFCM. Feature extraction uses ant lion optimizer (ALO) with Inception v3 model. For classifying AD, deep belief network (DBN) model is used. The WGANGP-DTL model is experimentally validated using benchmark 3D MRI datasets. WGANGP-DTL outperformed recent methods in experiments.

Automated climate prediction using pelican optimization based hybrid deep belief network for Smart Agriculture

A significant part of the economies of several nations throughout the world, including India, where agriculture accounts for 16% of the total economy. Due to their dynamic and turbulent natures, climate prediction in this region is the most difficult because statistical methodologies can't provide accurate predictions. Typically, weather forecasts were made using extraordinarily complex physics techniques that took into account various atmospheric conditions over a long time. These circumstances were usually unstable as a result of weather system disturbances, which led to the development of techniques for making unreliable predictions. In this study, an Automated Climate Prediction for Smart Agriculture is developed utilizing a Pelican Optimization-based Hybrid Deep Belief Network (ACP-POHDBN). The purpose of the ACP-POHDBN approach is to identify the appropriate meteorological conditions. The acp-POHDBN method uses the test data to calculate the appropriate weather conditions. Pre-processing, prediction, and hyperparameter tuning are the three key steps that make up the proposed ACP-POHDBN approach. The min-max normalization procedure is the primary procedure used by the ACP-POHDBN model to convert the meteorological data into a standard format. In addition to pre-processing data, the Deep Belief Network (DBN) model is used to forecast weather conditions. Finally, the DBN method's hyperparameters (epoch, batch size, and learning rate) may be properly tuned using the POA-based hyperparameter tuning technique. The development of a Pelican Optimization Algorithm (POA)-based hyperparameter optimizer for the process of predicting the climate demonstrates the originality of the study. The proposed algorithm over other recent approaches with maximum accuracy of 95.03%, sensitivity of 95.03%, specificity of 95.03%, and F-score of 95.03%.

Compared between support vector machine (SVM) and deep belief network (DBN) for multi-classification of Raman spectroscopy for cervical diseases

In this study, a minimally invasive test method for cervical cancer in vitro was proposed by comparing Raman spectroscopy with support vector machine (SVM) model and deep belief network (DBN) model. The serum Raman spectra of cervical cancer, hysteromyoma, and healthy people were collected. After data processing, SVM classification model and DBN classification model were built respectively. The experimental results show that when the DBN network algorithm is used, the sample test set can be divided accurately and the result of cross-validation is ideal. Compared with the traditional SVM algorithm, this method firstly screened the effective feature matrix from the data, and then classified the data. With high efficiency and accuracy, based on 445 samples collected, this method improved the accuracy by 13.93%±2.47% compared with the SVM method, and provided a new direction and idea for the in vitro diagnosis of cervical diseases.

Microblog Emotion Analysis Using Improved DBN Under Spark Platform

In order to solve the problems that traditional single-machine methods find it difficult to complete the task of emotion classification quickly, and the time efficiency and scalability are not high; a microblog emotion analysis method using improved deep belief network (DBN) under Spark platform is proposed. First, the Hadoop distributed file system is used to realize the distributed storage of text data, and the preprocessed data and emotion dictionary are converted into word vector representation based on the continuous bag-of-words model. Then, an improved DBN model is constructed by combining the adaptive learning method of DBN with the active learning method, and it is applied to the learning analysis of text word vectors. Finally, the data parallel optimization of the improved DBN model is realized, based on Spark platform to accurately and quickly obtain the emotion types of microblog texts. The experimental analysis of the proposed method based on the microblog text data set shows that the classification accuracy is more than 94%.

A Deep Learning Framework for Classifying and Evaluating Yoga Exercises

Yoga is a type of physical activity that combines physical postures with breathing techniques. Cardiovascular disease is the leading cause of illness and mortality. Yoga's potential involvement in cardiac recovery can be incorporated into cardiovascular rehabilitation. Classifying and evaluating yoga exercises using a deep learning framework plays a significant role in postoperative recovery. Home based rehabilitation often suffers from a lack of patient adherence to prescribed exercise routines, resulting in longer treatment times and higher healthcare expenses. This study proposes a framework for classifying and evaluating the quality of yoga exercises. The ConvNet followed by the deep belief network model is used to create quality scores for input movements to evaluate the quality of yoga asanas. Additionally, various machine learning algorithms are deployed to identify the best performing algorithm. The proposed framework is tested using the COCO dataset and the publicly available dataset from KAGGLE. The model is implemented using a three stage pipeline. In the first stage, an estimator and detector model is used for feature extraction. In the second stage, restricted Boltzmann machines are used for dimensionality reduction in two steps: The forward pass and the backward pass. Finally, the ConvNet followed by the deep belief network is used to classify yoga postures. The quaternion data is treated as a multivariate Gaussian variable to create Markov random fields. To quantify yoga asanas, the Markov random fields network is triggered, and the resulting quaternion point is compared to standard pose data, increasing the likelihood that the yoga position is correct. The importance of our study is that it allows participants to watch their movement in real-time and to obtain a numerical estimate of the accuracy of their yoga stance. Using this method, we were able to achieve a precision of 99.99%, which was previously unattainable on this dataset. Our model is more robust than previous models in terms of performance.

Statement of Removal

ABSTRACT Aiming at the problem that uneven illumination easily affects the facial expression recognition rate and reduces the facial expression recognition rate, a facial expression recognition algorithm based on local features and deep belief network (DBN) is proposed. Firstly, the non-uniform illumination invariant feature of LSH in facial expression image is extracted; Secondly, Gauss Laplace operator (log) is used to extract the edge detail features of facial expression; Then, the facial expression features fused by LSH and log are constructed; Finally, the DBN model is used to train the facial expression features fused by LSH and log, and the trained DBN model is used for facial expression recognition. Experiments on Jaffe facial expression database and Uighur facial expression database show that the algorithm has strong robustness and effectiveness.

Mayfly Optimization with Deep Belief Network-Based Automated COVID-19 Cough Classification Using Biological Audio Signals

The outbreak of the COVID-19 pandemic has made widespread testing a need for controlling the disease. Numerous recent investigations have shown that many people with COVID-19 show no outward signs of illness. As a result, these patients are more likely to unwittingly spread the virus because they will not take a COVID-19 test. In order to get tested, patients will need to travel to a lab, putting others at risk of exposure. Recent studies have shown that people with COVID-19 who are asymptomatic have distinctive coughs and breathing patterns compared to the general population. This prompted the study of cough and breath sounds in COVID-19 patients as a means of differentiating them from those of non-COVID lung infection patients and the general population. In this article, we present a robust, efficient, and extensible method for identifying symptomatic patterns in biological audio signals. Cough digitized audio files are subjected to spectral analysis via a stationary wavelet transform (SWT). The proposed model employs ADASYN technique to handle the class imbalance problem. Also, features like Mel-frequency cepstral coefficients (MFCCs), log frame energies, zero crossing rate (ZCR), and kurtosis are extracted. For classification process, deep belief network (DBN) model is utilized. Finally, mayfly optimization (MFO) algorithm is exploited for optimal hyper-parameter tuning of the DBN model. The experimental validation of the proposed model takes place using open access dataset. Proposed method is compared with other methods in terms accuracy, specificity, sensitivity, F1-Score, precision and recall. The experimental outcomes demonstrated the betterment of the proposed model over other recent state of art approaches.

Wearables-Assisted Smart Health Monitoring for Sleep Quality Prediction Using Optimal Deep Learning

Wearable devices such as smartwatches, wristbands, and GPS shoes are commonly employed for fitness and wellness as they enable people to observe their day-to-day health status. These gadgets encompass sensors to accumulate data related to user activities. Clinical act graph devices come under the class of wearables worn on the wrist to compute the sleep parameters by storing sleep movements. Sleep is very important for a healthy lifestyle. Inadequate sleep can obstruct physical, emotional, and mental health, and could result in several illnesses such as insulin resistance, high blood pressure, heart disease, stress, etc. Recently, deep learning (DL) models have been employed for predicting sleep quality depending upon the wearables data from the period of being awake. In this aspect, this study develops a new wearables-assisted smart health monitoring for sleep quality prediction using optimal deep learning (WSHMSQP-ODL) model. The presented WSHMSQP-ODL technique initially enables the wearables to gather sleep-activity-related data. Next, data pre-processing is performed to transform the data into a uniform format. For sleep quality prediction, the WSHMSQP-ODL model uses the deep belief network (DBN) model. To enhance the sleep quality prediction performance of the DBN model, the enhanced seagull optimization (ESGO) algorithm is used for hyperparameter tuning. The experimental results of the WSHMSQP-ODL method are examined under different measures. An extensive comparison study shows the significant performance of the WSHMSQP-ODL model over other models.

Diabetic Retinopathy Diagnosis Using Interval Neutrosophic Segmentation with Deep Learning Model

In recent times, Internet of Things (IoT) and Deep Learning (DL) models have revolutionized the diagnostic procedures of Diabetic Retinopathy (DR) in its early stages that can save the patient from vision loss. At the same time, the recent advancements made in Machine Learning (ML) and DL models help in developing Computer Aided Diagnosis (CAD) models for DR recognition and grading. In this background, the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network (ODBN) model i.e., NS-ODBN model for diagnosis of DR. The presented model involves Interval Neutrosophic Set (INS) technique to distinguish the diseased areas in fundus image. In addition, three feature extraction techniques such as histogram features, texture features, and wavelet features are used in this study. Besides, Optimal Deep Belief Network (ODBN) model is utilized as a classification model for DR. ODBN model involves Shuffled Shepherd Optimization (SSO) algorithm to regulate the hyperparameters of DBN technique in an optimal manner. The utilization of SSO algorithm in DBN model helps in increasing the detection performance of the model significantly. The presented technique was experimentally evaluated using benchmark DR dataset and the results were validated under different evaluation metrics. The resultant values infer that the proposed INS-ODBN technique is a promising candidate than other existing techniques.

Optimal Deep Belief Network Based Lung Cancer Detection and Survival Rate Prediction

The combination of machine learning (ML) approaches in healthcare is a massive advantage designed at curing illness of millions of persons. Several efforts are used by researchers for detecting and providing primary phase insights as to cancer analysis. Lung cancer remained the essential source of disease connected mortality for both men as well as women and their frequency was increasing around the world. Lung disease is the unrestrained progress of irregular cells which begin off in one or both Lungs. The previous detection of cancer is not simpler procedure however if it can be detected, it can be curable, also finding the survival rate is a major challenging task. This study develops an Ant lion Optimization (ALO) with Deep Belief Network (DBN) for Lung Cancer Detection and Classification with survival rate prediction. The proposed model aims to identify and classify the presence of lung cancer. Initially, the proposed model undergoes min-max data normalization approach to preprocess the input data. Besides, the ALO algorithm gets executed to choose an optimal subset of features. In addition, the DBN model receives the chosen features and performs lung cancer classification. Finally, the optimizer is utilized for hyperparameter optimization of the DBN model. In order to report the enhanced performance of the proposed model, a wide-ranging experimental analysis is performed and the results reported the supremacy of the proposed model.

Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

Data mining process involves a number of steps from data collection to visualization to identify useful data from massive data set. the same time, the recent advances of machine learning (ML) and deep learning (DL) models can be utilized for effectual rainfall prediction. With this motivation, this article develops a novel comprehensive oppositional moth flame optimization with deep learning for rainfall prediction (COMFO-DLRP) Technique. The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes. Primarily, data pre-processing and correlation matrix (CM) based feature selection processes are carried out. In addition, deep belief network (DBN) model is applied for the effective prediction of rainfall data. Moreover, COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning (COBL) with traditional MFO algorithm. Finally, the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN model. For demonstrating the improved outcomes of the COMFO-DLRP approach, a sequence of simulations were carried out and the outcomes are assessed under distinct measures. The simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.

Deep Learning Based Autonomous Transport System for Secure Vehicle and Cargo Matching

The latest 6G improvements secured autonomous driving's realism in Intelligent Autonomous Transport Systems (IATS). Despite the IATS's benefits, security remains a significant challenge. Blockchain technology has grown in popularity as a means of implementing safe, dependable, and decentralised independent IATS systems, allowing for more utilisation of legacy IATS infrastructures and resources, which is especially advantageous for crowdsourcing technologies. Blockchain technology can be used to address security concerns in the IATS and to aid in logistics development. In light of the inadequacy of reliance and inattention to rights created by centralised and conventional logistics systems, this paper discusses the creation of a blockchain-based IATS powered by deep learning for secure cargo and vehicle matching (BDL-IATS). The BDL-IATS approach utilises Ethereum as the primary blockchain for storing private data such as order and shipment details. Additionally, the deep belief network (DBN) model is used to select suitable vehicles and goods for transportation. Additionally, the chaotic krill herd technique is used to tune the DBN model’s hyperparameters. The performance of the BDL-IATS technique is validated, and the findings are inspected under a variety of conditions. The simulation findings indicated that the BDL-IATS strategy outperformed recent state-of-the-art approaches.