esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Formulation of Our Approach
\begin{split}&\forall i\in I_{c},\forall c\in\{1,2,\dots,C\}, \text{and }\forall k\in\{1,2,\dots,K\}\\ &\mathbf{\alpha}_{i}^{c}=\mathbf{z}_{i}^{c}\odot\mathbf{s}_{i}^{c}\\ &\mathbf{\alpha}_{i}^{c}=\mathbf{\Phi}\mathbf{\alpha}_{i}^{c}+\mathbf{\alpha}_{i _{c}}\quad\mathbf{h}_{i}^{c}=\mathbf{B}\mathbf{\alpha}_{i}^{c}+\mathbf{h}_{e_{i}}\\ &\pi_{k}^{c}\sim\text{Beta}(\pi_{k}^{c}|a_{0}/K,b_{0}(K-1)/K)\\ & z_{ik}^{c}\sim\text{Bernoulli}(z_{ik}^{c}|\pi_{k}^{c})\\ & s_{ik}^{c}\sim\mathcal{N}(s_{ik}^{c}|0,1/\lambda_{\phi}^{c})\\ &\mathbf{\phi}_{k}\sim\mathcal{N}(\mathbf{\phi}_{k}|\mathbf{0},1/\lambda_{ \phi_{0}}\mathbf{I}_{M})\ \ \mathbf{b}_{k}\sim\mathcal{N}(\mathbf{b}_{k}|\mathbf{0},1/\lambda_{b_{0}} \mathbf{I}_{C})\\ &\mathbf{a}_{e_{i}}\sim\mathcal{N}(\mathbf{\alpha}_{e_{i}}|\mathbf{0},1 /\lambda_{a}\mathbf{I}_{M})\ \ \mathbf{h}_{e_{i}}\sim\mathcal{N}(\mathbf{h}_{e_{i}}|\mathbf{0},1/\lambda_{h} \mathbf{I}_{C})\end{split}  \tag{6}\]  
We train the same \(\mathbf{z}_{i}^{c}\) and \(\mathbf{s}_{i}^{c}\) for both \(\mathbf{a}_{i}^{c}\) and \(\mathbf{h}_{i}^{c}\). We draw \(k\)th coefficients \(z_{ik}^{c}\) and \(s_{ik}^{c}\) of \(\mathbf{z}_{i}^{c}\) and \(\mathbf{s}_{i}^{c}\) from Bernoulli distribution and Gaussian distribution, respectively. \(\lambda\)'s are precision parameters of Gaussian priors in Equation (6). Bernoulli parameters \(\pi_{i}^{c}\) are drawn from the Beta distribution. To represent the \(k\)th column of the dictionary \(\mathbf{\Phi}\) and the classifier \(\mathbf{B}\), we use the notations \(\mathbf{\phi}_{k}\) and \(\mathbf{b}_{k}\), respectively, where \(\mathbf{0}\) is the zero vector of dimension \(M\) for the dictionary prior and of dimension \(C\) for classifier prior. The subscript '0' appearing in the expressions shows the hyperparameters belonging to prior distributions. We also modeled errors \(\mathbf{a}_{e_{i}}\) and \(\mathbf{h}_{e_{i}}\) for the construction of both \(\mathbf{a}_{i}^{c}\) and \(\mathbf{h}_{i}^{c}\). We further place noninformative Gamma hyperpriors over precision parameters, i.e., \(\lambda_{s}^{c}\sim\text{Gam}(c_{0},d_{0})\) and \(\lambda_{a},\lambda_{h}\sim\text{Gam}(e_{0},f_{0})\). In conjunction with the priors defined in Equation (6) and our model shown in Figure 1, we derive conditional probabilities of posterior parameters \((\mathbf{\phi}_{k},\mathbf{b}_{k},s_{ik}^{c},\mathbf{\pi}_{k}^{c},\mathbf{\pi}_{i}^{c})\) of our model in the coming sections.
# Periodontal Condition and Subgingival Microbiota Characterization in Subjects with Down Syndrome
## 1 Introduction  
The introduction Down syndrome (DS) is a congenital genetic disease, caused by the presence of an extra chromosome in par 21 [1], being the most common human aneuploidy among living births, with an estimated prevalence of one case per 800 births [2]. Its phenotypic expression, however, may be associated with significant genetic complexity [3]. Subjects with DS present a characteristic phenotype and intellectual disability [4], together with several systemic diseases [5], such as congenital heart disease and thyroid dysfunction, as well as a number of oral diseases [6, 7]. Furthermore, there is compromise in their innate immune response [8] and DS subjects suffer more frequently from oral infections, when compared with the general population [9, 10].  
Early onset forms of periodontitis and late onset of caries lesions represent the most frequent oral conditions associated with DS subjects [9], which may become further aggravated by their motor disability and manual dexterity compromise, that limits their performance in oral hygiene practices [11]. Several observational studies have reported high prevalence of gingivitis and periodontitis (ranging from 58-96%) at young ages (less than 35) in DS subjects [12]. In Brazil, in a study sample of 93 DS patients aged 6-20 years, only 9% showed a healthy periodontitis and 33% had periodontitis [13]; in another study including 64 DS subjects (mean age 23.8 years), 28.1% presented gingivitis and 71.9% periodontitis [14]. In the Netherlands, in a study group conformed by 182 DS subjects, 36.6% were diagnosed of periodontitis [15]. In addition, SD periodontitis patients have shown more severity, when compared with controls [16], and a higher impact in their quality of life [13]. In fact, in the latest classification of periodontal and peri-implant diseases and conditions [17], periodontitis in DS patients was included within the category \"periodonitis as a manifestation of a systemic disease,\" in which the systemic condition has a major impact in the onset and course of periodontitis, as clearly occurs in DS subjects [18].  
Furthermore, some authors have suggested a specific microbial profile associated to DS subjects with periodontitis [19; 20], raising the hypothesis that a distinctive pathogenic microbiota combined with an impaired host-response [21], may result in an earlier dysbiosis and onset of periodontitis [22]. However, the evidence supporting this hypothesis is very scarce and the reported results are highly heterogeneous [19; 20; 23]. In light of this limited information, we have designed this observational study aimed to evaluate the subgingival microbiota of DS subjects with different periodontal health status (periodontal health, gingivitis or periodontitis), by means of cultural and molecular microbiological methods.
## 3 Results and Discussion
### Physicochemical Properties  
The physicochemical properties of the five rice flows are stated in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analysis** & **TQR** & **Bario _[PERSON]_** & **Bario _Tuan_** & **Bario _Celum_** & \
\begin{tabular}{c} **Bario _Merah_** \\ _Sederhana_ \\ \end{tabular} \\ \hline Colour & & & & & \\ \hline L* & 93.62 \(\pm\) 0.04 a & 94.34 \(\pm\) 0.05 b & 84.84 \(\pm\) 0.07 c & 70.99 \(\pm\) 0.36 d & 75.26 \(\pm\) 0.04 e \\ \hline a* & \(-\)0.15 \(\pm\) 0.01 d & \(-\)0.21 \(\pm\) 0.02 d & 2.12 \(\pm\) 0.07 c & 2.75 \(\pm\) 0.06 b & 4.80 \(\pm\) 0.01 a \\ \hline b* & 5.27 \(\pm\) 0.07 d & 6.21 \(\pm\) 0.03 c & 14.46 \(\pm\) 0.13 a & 1.20 \(\pm\) 0.05 e & 7.91 \(\pm\) 0.04 b \\ \hline WAC (g/g) & 1.20 \(\pm\) 0.00 a & 1.26 \(\pm\) 0.04 a & 1.24 \(\pm\) 0.03 a & 1.21 \(\pm\) 0.00 a & 1.21 \(\pm\) 0.01 a \\ \hline Swelling capacity (g/g) & 23.53 \(\pm\) 0.10 a & 23.60 \(\pm\) 0.21 a & 22.97 \(\pm\) 0.0 b & 20.31 \(\pm\) 0.06 c & 22.91 \(\pm\) 0.20 b \\ \hline WSI (\%) & 2.32 \(\pm\) 0.01c & 1.83 \(\pm\) 0.08 d & 2.84 \(\pm\) 0.04 b & 2.70 \(\pm\) 0.10 b & 3.44 \(\pm\) 0.22 a \\ \hline Gelatinisation & & & & & \\ \hline T\({}_{\rm o}\) & 77.41 \(\pm\) 0.44 a & 71.43 \(\pm\) 0.46 c & 74.04 \(\pm\) 0.63 b & 74.89 \(\pm\) 0.22 b & 76.49 \(\pm\) 0.27 a \\ \hline T\({}_{\rm p}\) & 80.49 \(\pm\) 0.35 a & 77.03 \(\pm\) 0.15 d & 78.72 \(\pm\) 0.50 b & 78.24 \(\pm\) 0.41 c & 79.56 \(\pm\) 0.21 ab \\ \hline T\({}_{\rm f}\) & 83.22 \(\pm\) 0.33 a & 80.77 \(\pm\) 0.44 b & 84.45 \(\pm\) 0.67 a & 81.17 \(\pm\) 0.26 b & 84.38 \(\pm\) 0.68 a \\ \hline \(\Delta\)H\({}_{\rm gel}\) (J/g) & 0.79 \(\pm\) 0.07 c & 2.13 \(\pm\) 0.32 a & 1.77 \(\pm\) 0.08 ab & 2.59 \(\pm\) 0.61 a & 1.23 \(\pm\) 0.16 b \\ \hline Retrogradation & & & & & \\ \hline T\({}_{\rm o}\) & 41.80 \(\pm\) 0.52 c & 44.50 \(\pm\) 1.90 b & 50.79 \(\pm\) 1.35 a & 42.65 \(\pm\) 1.24 c & 45.90 \(\pm\) 0.29 b \\ \hline T\({}_{\rm p}\) & 44.36 \(\pm\) 1.55 b & 54.01 \(\pm\) 1.33 a & 56.15 \(\pm\) 1.27 a & 53.64 \(\pm\) 1.63 a & 54.91 \(\pm\) 0.63 a \\ \hline T\({}_{\rm f}\) & 48.71 \(\pm\) 1.37 b & 60.19 \(\pm\) 1.55 a & 61.02 \(\pm\) 1.32 a & 60.19 \(\pm\) 1.07 a & 61.25 \(\pm\) 1.89 a \\ \hline \(\Delta\)H\({}_{\rm pet}\) (J/g) & 0.13 \(\pm\) 0.03 c & 0.76 \(\pm\) 0.11 a & 0.19 \(\pm\) 0.04 c & 0.87 \(\pm\) 0.12 a & 0.42 \(\pm\) 0.08 b \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c} \hline \hline
**WAC = water absorption index, WSI = water solubility index, T\({}_{\rm o}\) = onset temperature, T\({}_{\rm p}\) = peak temperature, T\({}_{\rm f}\) = final temperature, \(\Delta\)H\({}_{\rm gel}\) = gelatinisation enthalpy and \(\Delta\)H\({}_{\rm rot}\) = retrogradation enthalpy, Mean \(\pm\) standard deviation, (\(n=3\)); Mean values in the same row with different superscripts are significantly different with \(p<0.05\).**  
\end{table}
Table 2: Physicochemical properties of Bario rice varieties compared with IQR rice.
## 2 Material and Methods  
### Site Description  
Our experimental forest stands with eddy towers were established in: a pure larch (_Larix gmelinii_) forest representative of the northern taiga region of central Siberia and a pure pine forest (_Pinus sylvestris_, hereafter as pine) representative of the middle taiga (Figure 1). Both forest ecosystems were at a post-fire successional stage and were very distinctive in their habitat and tree stand characteristics.  
The larch forest was located near the settlement of Tura, 600 km east of the Yenisei River, in the center of the Central Siberian Plateau (64\({}^{\circ}\)12\({}^{\prime}\) N, 100\({}^{\circ}\)27\({}^{\prime}\) E). This pure Gmelin larch forest (hereafter known as larch) was 105 years old, with mosses of _Pleurozium schreheri_ and _Aulacominium turgidum_, as well as the dwarf-shrubs of _Vaccinium vitis-idaea_, _V. uliginosum_ and the shrub of _Betula nana_ dominating the ground vegetation (Figure 2 left). Moss cover formed a thick porous layer of 10-25 cm in depth above the mineral soil and functioned as a heat insulator. The stand structure was characterized by a stem density of about 5480 living trees ha\({}^{-1}\), an LAI of 0.6 m\({}^{2}\) m\({}^{-2}\), a phytomass (dry weight) of 1.14 kg m\({}^{-2}\), a mean diameter of the trees at breast height of 6.5 cm and a mean tree height of 7.1 m. Individual tree crowns rarely overlap with one another [29].  
The stand was located on a slightly sloping ancient rock terrace of the Nizhnyaya Tunguska River with a northern aspect. Soil type according to the US Classification System (Soil Survey Staff, 1998) [30] was Gelisols with a permafrost table existing within the upper 1 m of the soil profile [31]. The soil texture of the surface-active layer was clay rich; each soil horizon had a high rock fragment ratio.  
Figure 1: Two study sites, Zotino and Tura, in central Siberia. The continuous permafrost area is shaded.  
The pine forest was located near the village of Zotino, about 30 km west of the Yenisei River at the eastern edge of the West Siberian Plain (60\({}^{\circ}\)44\({}^{\prime}\) N, 89\({}^{\circ}\)09\({}^{\prime}\) E). This forest was 215 years old, with lichens of the _Cladonia_, _Cladina_, and _Cetraria_ species dominating the ground cover (Figure 2 right). The stand structure was relatively homogenous with a stem density of 468 living trees per ha\({}^{-1}\), a basal area of 30 m\({}^{2}\) ha\({}^{-1}\), an LAI of 1.5 m\({}^{2}\) m\({}^{-2}\), a phytomass (dry weight) of 10.7 kg m\({}^{-2}\), a mean diameter of the trees at breast height of 28 cm, a mean tree height of 23 m, the crown cover was 30%-60% [32].  
The stand was located on slightly undulating, alluvial sandy soil with no underlying permafrost characterized according to the US Classification System [30] as a Pergelic Cryochrept [32]. The clay content in the mineral soil was less than 5%, and the soil was stone free. The layer of clay-rich material found at about 2.0 m depth was of potential importance for tree water supply in summer [33].
## 3 Results and Discussion
### Ultraviolet-Visible Spectroscopy (UV-Vis)  
UV-Vis absorption spectra of the thermally treated samples are illustrated in Figure 13. For pure LTA samples, a strong absorption is observed in the UV region that can be assigned to the direct bandgap transition in La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\)[17].  
For LTA-Pt powder, the characteristic UV absorption of the support is preserved. However, the platinum addition increases this UV absorption band's intensity and shifts the absorption edge to higher wavelengths. At the same time, this sample exhibits a strong light absorption in the visible region due to the d-d transitions in Pt-doped species of platinum nanoparticles [72; 73].  
The direct optical bandgap of the samples was calculated using the Tauc method. The obtained experimental values were 3.78 eV for LTA and 3.83 eV for LTA-Pt, being close to the literature data reported for La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) samples [74].
## 1 Introduction
Existing studies analyze individual modulations in LEO, MEO, or GEO systems, but there are not enough comparisons for multi-orbit systems [8] or comparisons that analyze the impact of error correction codes [10]. Given the rapid evolution of technology, understanding how different modulation types impact the performance of multi-orbit satellite communication links is vital [11]. This paper seeks to examine how various modulations affect the performance of multi-orbit satellite communication links, focusing on how each impacts the link budget and thus the overall reliability and efficiency of the communication system. It contributes to the existing literature by providing a detailed analysis of the sensitivity of the link budget in multi-orbital systems, considering the impact of different modulation schemes and error correction codes on the transmission performance. Unlike previous studies that treat the link budget individually for LEO, MEO, or GEO, this paper provides a direct comparison between them and evaluates the optimization of modulation in multi-orbital scenarios. Then, the analysis is extended by including the impact of error correction codes on the link margin, which is essential for improving the performance of satellite communication systems. Through this research, we aim to provide comprehensive insights into optimizing satellite communication systems, thereby supporting the ongoing advancement of this essential sector. The structure of the paper is as follows. In Section 2 we detail the mathematical model approached in the evaluation of the multi-orbit satellite link budget, pointing out all the existing notions of interest starting from a general case. Section 3 presents the resultsobtained for specific cases, all parameters of interest being highlighted. Section 4 presents some discussions based on the results obtained in multi-orbit scenarios, and the conclusions are presented in Section 5.
## 2 System Model and Design Goals
### Design Goals  
The goals of our proposed FGPQ scheme are described as follows:  
* Privacy preservation. As a profit company, the LBS data are considered as the LBS provider's own asset, which should be protected from disclosing. Therefore, the LBS data should be encrypted before being outsourced to fog nodes. For mobile users, a service query may contain some sensitive information, e.g., hobbies, current location and the location where mobile users will reach, which should also be protected from disclosing. Hence, mobile users should send the encrypted query request to the nearby fog node.
* Fine-grained query result. Besides the searching spatial range, the FGPQ scheme should satisfy the searching content, e.g., a hospital or a restaurant. That is, the query result should satisfy the given searching content and searching spatial range simultaneously.
* Efficiency. Owing to the mobility of mobile users, the low-latency is very critical for the LBS [9]. Hence, computational costs and communication delay should be as less as possible.
## 3 Results and Discussion
### Solubility of Mussel By-Product Hydrolysates  
Selected mussel by-product hydrolysates, representative of the different seasons and sampling sites, were analysed for solubility. Solubility of the mussel by-product hydrolysates ranged between 60%-100%, as seen in Figure 6. Change in pH had a differential effect on the solubility of different hydrolysates. Some hydrolyses showed the highest solubility at pH 2 (KHN18: 95.21% \(\pm\) 3.47%), whereas others showed the highest solubility at pH 10 (MBF19: 99.63% \(\pm\) 4.5%), and most showed very little change with change in pH. This shows that pH has very little influence on the solubility of mussel hydrolysate samples, thereby providing wider applicability in the selection of target food matrices. Similar results have been reported by other researchers for protein hydrolyses having a low degree of hydrolysis [36]. Enzymatic treatment often leads to the unfolding of proteins, exposing amino acids  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Average a\({}_{\rm w}\)** & **Standard Deviation (\(\pm\))** \\ \hline KHN18 & 0.35 & 0.0028 \\ MBF19 & 0.44 & 0.0035 \\ KHM19 & 0.33 & 0.004 \\ MBMay19 & 0.33 & 0.0005 \\ KH19 & 0.35 & 0.0028 \\ MBJuly19 & 0.37 & 0.008 \\ AJuly19 & 0.30 & 0.0005 \\ KHS19 & 0.25 & 0.0047 \\ KHO19 & 0.29 & 0.0115 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Water activity (a\({}_{\rm w}\)) measurements of mussel Protamex® hydrolysates. Nine samples from the three sampling sites, Mulroy bay, Killary harbour and Ardgroom, collected from November 2018 to October 2019, were used for analysis. Analysis was performed in triplicate (n = 3).  
Figure 5: Emulsion activity (%) of various mussel Protamex® hydrolysates (H) across a pH range of (2) to (10). All 9 samples from the three sampling sites, Mulroy bay (MB), Killary harbor (KH) and Ardgroom (A), collected from November 2018 to October 2019, were used for analysis. No significant difference was found between the average solubility values after [PERSON] post hoc analysis. Analysis was performed in triplicate (n = 3).  
buried within the protein structure [37]. Since Protamex is a relatively nonspecific serine protease, protein unfolding will differ, resulting in varied solubility of the samples, as seen in our study. Protein solubility of the samples is a prerequisite techno-functional property for designing many food products and is also necessary to ensure higher digestibility and bioavailability of the ingredients. A similar range of solubility was reported previously by authors working on octopus hydrolysates [38].
# Experimental Evaluation of the Packet Reception Performance of LoRa
## 5 Results and Analysis
### Measurement Results of Different LoRa Receivers  
To reduce the influence of other factors, ensure the consistency and scientificity of the experimental results, and make the experimental results convincing, we used the USRP B200 software radios to continuously transmit LoRa signals 10,000 times and used two commercial devices (SX1276 MB1 MAS and RN2483 Mote) as receivers. It should be noted that all experiments in our paper used SX1276 MB1 MAS and RN2483 Mote as receivers. The experimental results show that the packet reception performance of SX1276 MB1 MAS is similar to that of RN2483 Mote. For example, when SF = 7, BW = 125 kHz, CR = 4/8,  
Figure 9: The impact of different packet lengths on the packet reception performance of LoRa. (**a–h**) shows the results of the Packet Error Ratio for different packet lengths.  
and the packet length is 32 bytes, the results of the Packet Error Ratio of SX1276 MB1 MAS and RN2483 Note are similar, as shown in Figure 10. In other words, the experiments on evaluating the impact of the packet reception performance of LoRa have little to do with LoRa receivers produced by different manufacturers.
# The Role of Fungi in Food Production and Processing
## 2 Understanding the Research Prospects
### Methods for Fungal Identification and Characterization in Food Samples
#### 2.2.2 Culture-Based Methods  
Culture-based methods involve the isolation and cultivation of fungi from food samples on selective or non-selective agar media. Samples are plated onto suitable media, such as Sabouraud dextrose agar (SDA) or potato dextrose agar (PDA), and incubated under appropriate conditions to promote fungal growth. Colonies are then characterized based on their morphology, growth characteristics, and biochemical profiles. Additionally, selective media containing specific nutrients or inhibitors may be used to selectively isolate certain fungal species. While culture-based methods allow the isolation and identification of viable fungal species, they are time-consuming, labor-intensive, and may underestimate fungal diversity due to the inability to culture all the fungal species present in a sample.
## 4 Discussion  
### Comparison of Integer-Order and Fractional-Order Differential Spectra  
Traditional integer-order derivatives (e.g., first or second derivatives) apply a fixed transformation that can amplify noise along with the signal. In contrast, fractional-order differentiation (FOD) offers a flexible approach by allowing the derivative order to be adjusted. This flexibility enables FOD to enhance subtle spectral features while suppressing noise, providing a better balance between smoothing and detail extraction [11,12]. Such adaptability is especially important when dealing with fresh tea leaves, where water content and complex biochemical compositions pose additional challenges.
# Applications of Medical Informatics and Data Analysis Methods
## 4 Complex Machine Learning and Deep Learning Predictive Algorithms  
During the recent decades, mathematical statisticians have introduced new data analysis methods marked by the rapid expansion of computing efficiency and the advancement in storage capabilities. Examples of these are machine learning and deep learning networks. Many computational methods lie at the nexus of mathematical, statistical, and computational disciplines. Statistical methods often employ approaches that glean predictive capability from diverse and enormous databases of information. Emerging complex computational methods can provide impressive prediction models. However, it is unclear how widely these methods are applied in different medical domains [10; 11]. This Special Issue includes four articles that focus on these predictive methods.  
It is difficult to predict a patient's outcome with serial data that is collected irregularly, including medications, treatments, and laboratory tests. Typical deep learning methods can be used to analyze serial data. However, they must be improved to handle irregularly sampled serial datasets. In their study, [PERSON] and colleagues [12] investigate the accuracy of the phased long-term short-term memory (phased-LSTM) deep learning method in the prediction of patients with prostate cancer who might have castration-resistant prostate cancer (CRPC). The authors found that the phased-LSTM model was able to predict the CRPC outcome with 91.6% and 96.9% using 120 and 360 days of data, respectively.  
The paper _A Comparison of Deep Learning Methods for ICD Coding of Clinical Records_ authored by [PERSON] and colleagues [13] presents a survey of various deep learning methods for text classification in a hierarchical framework for the domain of medical documents. Methods based on exploiting the taxonomy structure and also flat methods are discussed. These methods are evaluated on publicly available datasets corresponding to ICD-9 and ICD-10 coding, respectively.  
In their contribution, [PERSON] and co-authors [14] demonstrate the particularities of applying machine learning techniques in the field of healthcare. They focus on cervical assessment, where the goal is to predict the potential presence of cervical pain in patients affected with whiplash diseases. Using a sample of 302 patients, they compared several predictive models, including logistic regression, support vector machines, k-nearest neighbors, gradient boosting, decision trees, random forest, and neural network algorithms.  
[PERSON] and [PERSON] [15] present in their article _Taylor Bird Swarm Algorithm Based on Deep Belief Network for Heart Disease Diagnosis_ an approach to classify medical data for medical decision making. The method uses a feature selection step, where a sparse Fuzzy-c-mean (FCM) approach is used to select the significant features. Then, the selected features are passed into a deep belief network, which is trained using the Taylor-based bird swarm algorithm. The result of the analysis shows that the method is a promising approach.