**7. Heat shock protein used in immune intervention**

Studies in experimental animals have shown that use of a 65-kDa heat shock protein can pre‐ vent diabetes [48]. A specific peptide, Diapep 277, seems to be the active component and this peptide has been tried with interesting effects.

Clinical trials in humans have shown that sc administration of Diapep 277 may preserve beta cell function in adults [49]. Thus 35 patients with type 1 diabetes and basal C-peptide above 0.1 nmol/L were assigned to subcutaneous injections of 1 mg Diapep277 and 40 mg mannitol in vegetable oil The primary endpoint was glucagon-stimulated C-peptide production. At 10 months, mean C-peptide concentrations had fallen in the placebo group (n=16) but were main‐ tained in the DiaPep277 group (n=15; p=0.039). Need for exogenous insulin was higher in the placebo than in the DiaPep277 group. There were no o adverse events. The treatment of newly diagnosed T1D adults with DiaPep277 seemed to preserve residual insulin secretion through induction of a shift from Thr-1 to Thr-2 cytokines. However, the efficacy seen in adults could not be confirmed in children and adolescents with T1D [50,51] in spite of interesting immu‐ nologcval results [52]. In a recent Phase III trial no immunological difference could be found between adults treated with Diapep 277 or those treated with placebo [53]. Treatment with Diapep 277 seemed to preserve C.peptide but only C-peptide after Glucagon stimulation, but not after Mixed Meal Tolerance Test [54]. Thus it is still unclear whether Diapep 277 has a place or not as future intervention to preserve residual insulin secretion in adults.


**FRUITS LIST**

The Fruits list includes fresh, frozen, canned, and dried fruits and fruit juices. 1 fruit choice = 15 grams carbohydrate, 0 grams protein, 0 grams fat, and 251.04 KJ.

In general, 1 fruit choice is:

• 1/2 cup of canned or fresh fruit or unsweetened fruit juice

• 1 small fresh fruit (113.4 g)

• 2 tablespoons of dried fruit

*NOTE: The weight listed includes skin, core, seeds, and rind; 1cup(metric) equal to 250 ml.*

### **MILK LIST**

The Milk list groups milks and yogurts based on the amount of fat they have (fat-free/low fat, reduced fat, and whole).

• Cheeses are on the Meat and Meat Substitutes list (because they are rich in protein).

• Cream and other dairy fats are on the Fats list.

*NOTE: In general, one milk choice is 1 cup (8 fluid ounces or ½ pint) milk or yogurt.*


*Edited by Hasan Tosun, Necmi Gürsakal and Asli Sebatli-Saglam*

This book presents recent research on risk management in the construction industry. It consists of eight chapters, each of which contains valuable information on specified areas of risk management engineering. Topics addressed include identifying, analyzing, assessing, controlling, and monitoring risk for planning and construction stages; risk management methods used in the construction industry and case studies; risk management for buildings, dams, highways, railways, nuclear power plants, underground structures, energy facilities, and their appurtenant structures. This book provides a comprehensive overview of the latest information in risk management in the construction industry.

*Assed Haddad, Civil Engineering Series Editor*

ISBN 978-1-83769-309-2

Risk Management in Construction - Recent Advances

ISSN 3029-0287

Published in London, UK © 2024 IntechOpen © Jarwis / iStock

IntechOpen Series Civil Engineering, Volume 2


CNN Approaches for Time Series Classification DOI: http://dx.doi.org/10.5772/intechopen.81170

The first category of time series classification approaches consists of building a

The second category relies on developing distance functions to measure the similarity (or dissimilarity) between two time series and on selecting a good classifier, such as dynamic time warping (DTW) distance [9, 10]. But these approaches

The third category consists of extracting meaningful features from the time series. Examples of such approaches include the discrete Fourier transform (DFT) [11], the Short-time Fourier transform (STFT) [12], the discrete wavelet transform (DWT), principal component analysis (PCA), singular value decomposition (SVD),

Meanwhile, automatic feature-based approaches using deep learning models rely have been successfully applied to time series classification, classification problems, especially convolutional neural networks (CNNs) which are regarded as the most successful and commonly used deep learning model. In [5, 6], authors address the problematic of speech recognition whereby speech signals have similar patterns within different frequency band locations which convey a different meaning. A solution to this problem is to employ a limited weight sharing CNN [6] where weight sharing is limited only to local filters which are close to each other and which are pooled together in the subsampling layer. Another approach based on tiled CNN architecture with a pre-training stage (an unsupervised learning algorithm named topographic ICA) was proposed by [15], which showed its superiority over traditional CNN on small time series datasets. A tiled CNN [16] is a CNN which unties weights locally and uses a regular "tiled" pattern of tied weights that requires only that hidden units k steps away from each other to have tied weights. Another relevant CNN architecture for time series classification named multi-scale

convolutional neural network (MCNN) [17] was introduced where each of the three transformed versions of the input (which will be discussed in Section 3.1) is fed into a branch i.e., a set of consecutive convolutional and pooling layers, resulting in three outputs which are concatenated and further fed into more convolutional and pooling layers, fully connected layers and a softmax layer to generate the final output. Training all parameters is done jointly using back-propagation. Another attempt to enhance time series classification was proposed in [18], which employs the same idea of multiple branches within the CNN architecture, except that the input is not a different transformed version of the time series signal fed into each branch, but rather a duplicate of the same time series signal fed into all the branches (three branches). However, different convolutional filter sizes are applied per branch in order to capture the multi-scale characteristics of the time series. Two other CNN proposals to time series classification were suggested in [19], namely fully convolutional networks (FCN) without subsampling layers, and residual networks (ResNet). FCNs [20] are defined as networks which have convolutional layers only and no fully-connected layers, whereas ResNet [21] is a type of specialized neural network that solves the "vanishing gradient" problem when having many layers within the network, by using residual blocks which take advantage of residual mapping to preserve inputs. By adding batch normalization layers into FCN and ResNet, and by replacing the fully connected layers with a global pooling layer in the FCN, these two deep learning models seem to yield comparable or better results than MCNN [17]. An ensemble method of deep learning networks named LSTM-FCN is proposed in [22] is proposed and consists of feeding the same time

model for each class by fitting its parameters to that class. Examples of such approaches are the autoregressive (AR) model [7] and the hidden Markov model (HMM) [8] which are limited to stationary and symbolic non-stationary time series

respectively.

58

are computationally expensive.

sparse coding [13], and shapelets [14].

Time Series Analysis - Data, Methods, and Applications

series input into two branches: an FCN and Long Short Term Recurrent Neural Network (LSTM) block [23], producing two outputs which are concatenated and then passed onto a softmax classification layer. Another attempt to helps the CNN converge faster and better to the minima was made by Guennec et al. [24] who propose to perform data-augmentation techniques (further described in Section 3.1) and pre-train each layer in an unsupervised manner (using an auto-encoder) using unlabeled training time series from different datasets. For multivariate time series, only few research papers based on CNNs were published (such as [3, 4, 25, 26]). Zheng et al. [25] proposed a multi-channels deep convolution neural network (MC-DCNN), each branch of which takes a single dimension of the multivariate time series as input and learns features individually. Then the MC-DCNN model combines the learnt features of each branch and feeds them into a fully connected layer to perform classification. And, to further improve the performance, authors also suggested to pre-train the MC-DCNN first by applying an unsupervised initialization via the convolutional auto-encoder method. Meanwhile, a different CNN architecture for multivariate time series classification was introduced in [3, 4, 26], which treats the 3-, 12-, and 9-variate time series inputs (in [3, 4, 26] respectively) as a 3-, 12-, and 9-channel inputs and convolves them as a whole instead of convolving each channel of the input separately as performed in [25]. Authors of this architecture argue that, by separating multivariate time series into univariate ones just as in [25], the interrelationship between different univariate time series may be lost and thus will not be mined/extracted.

In this paper, we aim at presenting a review on our CNN approaches for time series classification. Our review discusses our CNN contributions at the data-level and at the algorithm-level. Our paper is organized as follows. In Section 2, some preliminaries about time series are introduced. In Section 3 reviews existent datalevel techniques are presented, our data-level technique is reviewed, and experiments as well as results of our technique are laid out. Section 4 describes our algorithm-level approaches for time series classification, with experiments conducted and results analyzed. Section 4.3.3 concludes our paper with future perspectives.


*Edited by Robert Y. Ning*

Desalination Update illustrates the growing research and development activities in the field of desalination of water. The chapters in this book also show the close link in the supply of water and supply of power. Power is needed to desalinate water, and water is needed to produce power via steam and cooling water. As the world is becoming increasingly in need of water and power, the education of generations of new workers in these technologies makes the publications of these books of rising importance. Students and specialists alike will find branching strands in this field of development worthy of dedication of careers. Never has shrinking essential resources and exploding needs confront mankind as much as water. Excellent reviews in this book provide keywords, concepts, and current knowledge and status of practice useful for teaching and continued evolution.

Photo by MADDRAT / DollarPhoto

Desalination Updates

Desalination Updates

*Edited by Robert Y. Ning*


4.5.3. Ferric Reducing Power Assay

In the ferric reducing power test, is measured the reduction in the bright red color potassium ferricyanide to the intense blue color ferric ferrocyanide (Prussian blue), which occurs by means of antioxidants in acidic environments, with an increase in absorbance at 700 nm [84].

The ability of all the newly synthesized compounds to reduce iron(III) to iron(II) was determined by the modified Oyaizu method [92–94]. The sample solutions were prepared in DMSO at a concentration of 500 μM. Two milliliters of each tested compound solution were mixed with 2 mL of 0.2 M phosphate buffer (pH 6.6) and 2 mL of 1% K3[Fe(CN)6] solution. After 20 min incubation at 50 ◦C, 2 mL of 10% trichloroacetic acid solution was added, and the mixture was centrifuged for 15 min at 4500 rpm. Finally, 2 mL of the upper layer was mixed with 2 mL of deionized water and 0.4 mL of 0.1% FeCl3 solution, and after 5 min the absorbance was recorded at 700 nm with an Analytik Jena Specord 40 UV/VIS spectrophotometer. All analyses were performed on three replicates, and the results were averaged and compared with those of antioxidant standards used, i.e., AA, BHA, and BHT.


**1. Introduction**

Emotion plays an important role in the daily interpersonal interactions and is considered an essential skill for human communication [1]. It helps humans to understand the opinions of others by conveying feelings and giving feedback to people. Emotion has many useful benefits of affective computing and cognitive activities such as rational decision making, perception and learning [2]. It has opened up an exhilarating research agenda because constructing an intelligent robotic dialogue system that can recognize emotions and precisely respond in the manner of human conversation is presently arduous. The requirement of emotion recognition is steadily increasing with the pervasiveness of intelligent systems [3]. Huawei intelligent video surveillance systems, for instance, can support real-time tracking of a person in a distressed phase through emotion recognition. The capability to recognize human emotions is considered an essential future requirement of intelligent systems that are inherently supposed to interact with people to a certain degree of emotional intelligence [4]. The necessity to develop emotionally intelligent systems is exceptionally important for the modern society of the internet of things (IoT) because such systems have great impact on decision making, social communication and smart connectivity [5].

Practical applications of emotion recognition systems can be found in many domains such as audio/video surveillance [6], web-based learning, commercial applications [7], clinical studies, entertainment [8], banking [9], call centers [10], computer games [11] and psychiatric diagnosis [12]. In addition, other real applications include remote tracking of persons in a distressed phase, communication between human and robots, mining sentiments of sport fans and customer care services [13], where emotion is perpetually expressed. These numerous applications have led to the

development of emotion recognition systems that use facial images, video files or speech signals [14]. In particular, speech signals carry emotional messages during their production [15] and have led to the development of intelligent systems habitually called speech emotion recognition systems [16]. There is an avalanche of intrinsic socioeconomic advantages that make speech signals a good source for affective computing. They are economically easier to acquire than other biological signals like electroencephalogram, electrooculography and electrocardiograms [17], which makes speech emotion recognition research attractive [18]. Machine learning algorithms extract a set of speech features with a variety of transformations to appositely classify emotions into different classes. However, the set of features that one chooses to train the selected learning algorithm is one of the most important tools for developing effective speech emotion recognition systems [3,19]. Research has suggested that features extracted from the speech signal have a great effect on the reliability of speech emotion recognition systems [3,20], but selecting an optimal set of features is challenging [21].

Speech emotion recognition is a difficult task because of several reasons such as an ambiguous definition of emotion [22] and the blurring of separation between different emotions [23]. Researchers are investigating different heterogeneous sources of features to improve the performance of speech emotion recognition systems. In [24], performances of Mel-frequency cepstral coefficient (MFCC), linear predictive cepstral coefficient (LPCC) and perceptual linear prediction (PLP) features were examined for recognizing speech emotion that achieved a maximum accuracy of 91.75% on an acted corpus with PLP features. This accuracy is relatively low when compared to the recognition accuracy of 95.20% obtained for a fusion of audio features based on a combination of MFCC and pitch for recognizing speech emotion [25]. Other researchers have tried to agglutinate different acoustic features with the optimism of boosting accuracy and precision rates of speech emotion recognition [25,26]. This technique has shown some improvement, nevertheless it has yielded low accuracy rates for the fear emotion in comparison with other emotions [25,26]. Semwal et al. [26] fused some acoustic features such as MFCC, energy, zero crossing rate (ZCR) and fundamental frequency that gave an accuracy of 77.00% for the fear emotion. Similarly, Sun et al. [27] used a deep neural network (DNN) to extract bottleneck features that achieved an accuracy of 62.50% for recognizing the fear emotion. The overarching objective of this study was to construct a set of hybrid acoustic features (HAFs) to improve the recognition of the fear emotion and other emotions from speech signal with high precision. This study has contributed to the understanding of the topical theme of speech emotion recognition in the following unique ways.

The content of this paper is succinctly organized as follows. Section 1 provides the introductory message, including the objective and contributions of the study. Section 2 discusses the related studies in chronological order. Section 3 designates the experimental databases and the study methods. The details of the results and concluding statements are given in Sections 4 and 5 respectively.


**5. Conclusions**

**Author Contributions:** Conceptualization, Z.Y. and J.Y.; methodology, Z.Y. and H.W. (Hai Wei); software, Z.Y. and X.G.; validation, Z.Y. and H.W. (Huichang Wu); formal analysis, Z.Y. and H.W. (Hai Wei); investigation, Z.Y., T.H. and J.W.; resources, Z.Y. and J.W.; data curation, Z.Y.; writing—original draft preparation, Z.Y. and X.G.; writing—review and editing, Z.Y., X.G. and J.Y.; visualization, J.Y. and X.G.; supervision, H.W. (Huichang Wu); and J.W.; project administration, H.W. (Huichang Wu); funding acquisition, H.W. (Huichang Wu); All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by China Agriculture Research System—Green Manure, grant number CARS-22; Basic Scientific Research Service Fee of Chinese Academy of Agricultural Sciences, grant number S202012; Innovation Engineering of the Chinese Academy of Agricultural Sciences— Primary Processing Equipment for Major Grain and Economic Crops, grant number 31-NIAM-09.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on-demand from the first author at (youzhaoyan@caas.cn).

**Acknowledgments:** The authors would like to acknowledge Wuhu Qingyijiang Seed Industry Co., Ltd. for its strong support in the trial production and processing of the Chinese milk vetch seed combine harvester, and for providing the test site for this study.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**