deep belief network

Multivariate time series (MTS) play essential roles in daily life because most real-world time series datasets are multivariate and rich in time-dependent information. Traditional forecasting methods for MTS are time-consuming and filled with complicated limitations. One efficient method being explored within the dynamical systems is the extended short-term memory networks (LSTMs). However, existing MTS models only partially use the hidden spatial relationship as effectively as LSTMs. Shallow LSTMs are inadequate in extracting features from high-dimensional MTS; however, the multilayer bidirectional LSTM (BiLSTM) can learn more MTS features in both directions...

The commercial adoption of BCI technologies for both clinical and non-clinical applications is drawing scientists to the creation of wearable devices for daily living. Emotions are essential to human existence and have a significant impact on thinking. Emotion is frequently linked to rational decision-making, perception, interpersonal interaction, and even basic human intellect. The requirement for trustworthy and implementable methods for the detection of individual emotional responses is needed with rising attention of the scientific community towards the establishment of some significant emotional connections among people and computers...

Histopathological grading of the tumors provides insights about the patient's disease conditions, and it also helps in customizing the treatment plans. Mitotic nuclei classification involves the categorization and identification of nuclei in histopathological images based on whether they are undergoing the cell division (mitosis) process or not. This is an essential procedure in several research and medical contexts, especially in diagnosis and prognosis of cancer. Mitotic nuclei classification is a challenging task since the size of the nuclei is too small to observe, while the mitotic figures possess a different appearance as well...

Breast cancer (BC) has affected many women around the world. To accomplish the classification and detection of BC, several computer-aided diagnosis (CAD) systems have been introduced for the analysis of mammogram images. This is because analysis by the human radiologist is a complex and time-consuming task. Although CAD systems are used to primarily analyze the disease and offer the best therapy, it is still essential to enhance present CAD systems by integrating novel approaches and technologies in order to provide explicit performances...

In recent years, the increased population has led to an increase in the demand for various industrially processed edibles and other consumable products. These industries regularly alter the proteins found in raw materials to generate more commercially viable end-products in order to keep up with consumer demand. These modifications result in a substance that may cause allergic reactions in consumers, thereby creating a protein allergen. The detection of such proteins in various substances is essential for the prevention, diagnosis and treatment of allergic conditions...

[This retracts the article DOI: 10.1155/2022/8898100.].

In the world, grapes are considered as the most significant fruit, and it comprises various nutrients, like Vitamin C and it is utilized to produce wines and raisins. The major six general grape leaf diseases and pests are brown spots, leaf blight, downy mildew, anthracnose, and black rot. However, the existing manual detection methods are time-consuming and require more efforts. In this paper, an effectual grape leaf disease finding and percentage of pesticide detection approach is devised usingan optimized deep learning scheme...

BACKGROUND: Driving fatigue is one of the main factors leading to traffic accidents. So, it is necessary to detect driver fatigue accurately and quickly. NEW METHOD: To precisely detect driving fatigue in a real driving environment, this paper adopts a classification method for driving fatigue based on the wavelet scattering network (WSN). Firstly, electroencephalogram (EEG) signals of 12 subjects in the real driving environment are collected and categorized into two states: fatigue and awake...

BACKGROUND: Accurate grading of semantic characteristics is helpful for radiologists to determine the probabilities of the likelihood of malignancy of a pulmonary nodule. Nevertheless, because of the complex and varied properties of pulmonary nodules, assessing semantic characteristics (SC) is a difficult task. METHOD: In this paper, we first analyze a set of important semantic characteristics of pulmonary nodules and extract the important SCs relating to pulmonary nodule malignancy by Pearson's correlation approach...

The IoT offered an enormous number of services with the help of multiple applications so it faces various security-related problems and also heavy malicious attacks. Initially, the IoT data are gathered from the standard dataset as Message Queuing Telemetry Transport (MQTT) set. Further, the collected data are undergone the pre-processing stage, which is accomplished by using data cleaning and data transformation. The resultant processed data is given into two models named (i) Autoencoder with Deep Belief Network (DBN), in which the optimal features are selected from Autoencoder with the aid of Modified Archimedes Optimization Algorithm (MAOA)...

OBJECTIVE: For several years, computer technology has been utilized to diagnose lung nodules. When compared to traditional machine learning methods for image processing, deep-learning methods can improve the accuracy of lung nodule diagnosis by avoiding the laborious pre-processing step of the picture (extraction of fake features, etc.). Our goal is to investigate how well deep-learning approaches classify lung nodule malignancy. METHOD: We evaluated the performance of deep-learning methods on lung nodule malignancy classification via a systematic literature search...

Estimation of continuous motion of human joints using surface electromyography (sEMG) signals has a critical part to play in intelligent rehabilitation. Traditional methods always use sEMG signals as inputs to build regression or biomechanical models to estimate continuous joint motion variables. However, it is challenging to accurately estimate continuous joint motion in new subjects due to the non-stationarity and individual differences in sEMG signals, which greatly limits the generalisability of the method...

Depth estimation is an ill-posed problem; objects of different shapes or dimensions, even if at different distances, may project to the same image on the retina. Our brain uses several cues for depth estimation, including monocular cues such as motion parallax and binocular cues such as diplopia. However, it remains unclear how the computations required for depth estimation are implemented in biologically plausible ways. State-of-the-art approaches to depth estimation based on deep neural networks implicitly describe the brain as a hierarchical feature detector...

As an important branch of artificial intelligence and machine learning, deep learning (DL) has been widely used in various aspects of cancer auxiliary diagnosis, among which cancer prognosis is the most important part. High-accuracy cancer prognosis is beneficial to the clinical management of patients with cancer. Compared with other methods, DL models can significantly improve the accuracy of prediction. Therefore, this article is a systematic review of the latest research on DL in cancer prognosis prediction...

In recent years, the industrial network has seen a number of high-impact attacks. To counter these threats, several security systems have been implemented to detect attacks on industrial networks. However, these systems solely address issues once they have already transpired and do not proactively prevent them from occurring in the first place. The identification of malicious attacks is crucial for industrial networks, as these attacks can lead to system malfunctions, network disruptions, data corruption, and the theft of sensitive information...

Milk powder can provide the necessary nutrients for the growth of infants, and the level of its energy value is an important factor in the measurement of its nutritional value. Therefore, the measurement of the energy value in milk powder is of great significance for the nutritional health of infants. In this study, samples of 32 different brands of milk powder were selected for spectral analysis, and laser-induced breakdown spectroscopy (LIBS) combined with deep belief network (DBN), back propagation (BP) neural network, and long short-term memory (LSTM) models was used to achieve quantitative analysis of the energy value of the milk powder...

In this study, a deep quantum neural network (DQNN) based on the Lion-based Coot algorithm (LBCA-based Deep QNN) is employed to predict COVID-19. Here, the genome sequences are subjected to feature extraction. The fusion of features is performed using the Bray-Curtis distance and the deep belief network (DBN). Lastly, a deep quantum neural network (Deep QNN) is used to predict COVID-19. The LBCA is obtained by integrating Coot algorithm and LOA. The COVID-19 predictions are done with mutation points. The LBCA-based Deep QNN outperformed with testing accuracy of 0...

Light olefins, as the backbone of the chemical and petrochemical industries, are produced mainly via steam cracking route. Prediction the of effects of operating variables on the product yield distribution through the mechanistic approaches is complex and requires long time. While increasing in the industrial automation and the availability of the high throughput data, the machine learning approaches have gained much attention due to the simplicity and less required computational efforts. In this study, the potential capability of four powerful machine learning models, i...

PURPOSE: This study investigated general and subject-specific motivational beliefs from the perspectives of self-determination theory (i.e. intrinsic, identified, introjected, and extrinsic motivation) and achievement goal theory (i.e. mastery, performance-approach, and -avoidance goal orientation including the respective classroom goal structures), their interrelations, their stability over time, and their associations with desirable outcomes (i.e. deep-level learning strategies, self-concept of ability)...

[This retracts the article DOI: 10.1155/2022/6389069.].