Elements of digitalization in organization of traceability of poultry processing products

The paper provides data on the systematization of poultry processing products, highlighting the basic and specific principles of assigning products to homogeneous species and groups. The performed analysis made it possible to assign a digital or letter designation to each product classification feature. In the course of the research, the order of presentation of information about the properties of products was determined when compiling the name of the product, as well as in the composition of the alphanumeric code. Schemes for drawing up alphanumeric codes for general food products – slaughter products and processed products of poultry slaughter products – have been developed and presented.

Aspect Based Sentiment Analysis for E-Commerce Shopping Website

Sentiment Analysis is that the most ordinarily used approach to research knowledge that is within the form of text and to identify sentiment content from the text. Opinion Mining is another name for sentiment analysis. a good vary of text data is getting generated within the form of suggestions, feedback, tweets, and comments. E-Commerce portals area unit generating tons of data. Every day within the form of customer reviews. Analyzing E-Commerce data can facilitate on-line retailers to grasp customer expectations, offer an improved searching expertise, and to extend sales. Sentiment Analysis can be used to identify positive, negative, and neutral information from the customer reviews. Researchers have developed a lot of techniques in Sentiment Analysis. Keywords: Sentiment analysis, Sentiment classification, Feature selection, Emotion detection, Customer Reviews;

Thermal face recognition under different conditions

Background A thermal face recognition under different conditions is proposed in this article. The novelty of the proposed method is applying temperature information in the recognition of thermal face. The physiological information is obtained from the face using a thermal camera, and a machine learning classifier is utilized for thermal face recognition. The steps of preprocessing, feature extraction and classification are incorporated in training phase. First of all, by using Bayesian framework, the human face can be extracted from thermal face image. Several thermal points are selected as a feature vector. These points are utilized to train Random Forest (RF). Random Forest is a supervised learning algorithm. It is an ensemble of decision trees. Namely, RF merges multiple decision trees together to obtain a more accurate classification. Feature vectors from the testing image are fed into the classifier for face recognition. Results Experiments were conducted under different conditions, including normal, adding noise, wearing glasses, face mask, and glasses with mask. To compare the performance with the convolutional neural network-based technique, experimental results of the proposed method demonstrate its robustness against different challenges. Conclusions Comparisons with other techniques demonstrate that the proposed method is robust under less feature points, which is around one twenty-eighth to one sixtieth of those by other classic methods.

A hybrid predictive framework for evaluating P2P credit risks

PurposeDespite better accessibility and flexibility, peer-to-peer (P2P) lending has suffered from excessive credit risks, which may cause significant losses to the lenders and even lead to the collapse of P2P platforms. The purpose of this research is to construct a hybrid predictive framework that integrates classification, feature selection, and data balance algorithms to cope with the high-dimensional and imbalanced nature of P2P credit data.Design/methodology/approachAn improved synthetic minority over-sampling technique (IMSMOTE) is developed to incorporate the randomness and probability into the traditional synthetic minority over-sampling technique (SMOTE) to enhance the quality of synthetic samples and the controllability of synthetic processes. IMSMOTE is then implemented along with the grey relational clustering (GRC) and the support vector machine (SVM) to facilitate a comprehensive assessment of the P2P credit risks. To enhance the associativity and functionality of the algorithm, a dynamic selection approach is integrated with GRC and then fed in the SVM's process of parameter adaptive adjustment to select the optimal critical value. A quantitative model is constructed to recognize key criteria via multidimensional representativeness.FindingsA series of experiments based on real-world P2P data from Prosper Funding LLC demonstrates that our proposed model outperforms other existing approaches. It is also confirmed that the grey-based GRC approach with dynamic selection succeeds in reducing data dimensions, selecting a critical value, identifying key criteria, and IMSMOTE can efficiently handle the imbalanced data.Originality/valueThe grey-based machine-learning framework proposed in this work can be practically implemented by P2P platforms in predicting the borrowers' credit risks. The dynamic selection approach makes the first attempt in the literature to select a critical value and indicate key criteria in a dynamic, visual and quantitative manner.

Fused Deep Features-Based Grape Varieties Identification Using Support Vector Machine

Proper identification of different grape varieties by smart machinery is of great importance to modern agriculture production. In this paper, a fast and accurate identification method based on Canonical Correlation Analysis (CCA), which can fuse different deep features extracted from Convolutional Neural Network (CNN), plus Support Vector Machine (SVM) is proposed. In this research, based on an open dataset, three types of state-of-the-art CNNs, seven species of deep features, and a multi-class SVM classifier were studied. First, the images were resized to meet the input requirements of a CNN. Then, the deep features of the input images were extracted by a specific deep features layer of the CNN. Next, two kinds of deep features from different networks were fused by CCA to increase the effective classification feature information. Finally, a multi-class SVM classifier was trained with the fused features. When applied to an open dataset, the model outcome shows that the fused deep features with any combination can obtain better identification performance than by using a single type of deep feature. The fusion of fc6 (in AlexNet network) and Fc1000 (in ResNet50 network) deep features obtained the best identification performance. The average F1 Score of 96.9% was 8.7% higher compared to the best performance of a single deep feature, i.e., Fc1000 of ResNet101, which was 88.2%. Furthermore, the F1 Score of the proposed method is 2.7% higher than the best performance obtained by using a CNN directly. The experimental results show that the method proposed in this paper can achieve fast and accurate identification of grape varieties. Based on the proposed algorithm, the smart machinery in agriculture can take more targeted measures based on the different characteristics of different grape varieties for further improvement of the yield and quality of grape production.

Algorithm for Key Classification Feature Selection of Big Data Based on Henie Theorem

With the extensive application of the database system, the available data of enterprises or individuals are expanding, and the existing technology is difficult to meet the data analysis requirements of the big data age. Therefore, the selection of key classification features of big data needs to be carried out. However, when the key classification features of big data are selected by the current algorithm, the distance between the samples can not be given accurately, and there is a large error in the classification. To solve this problem, a key classification feature selection algorithm based on Henie theorem is proposed. In this algorithm, the second programming algorithm is firstly used to make the weighted distance between the intra-class and the inter-class as the quadratic term and linear term parameter in the target function, and balance the relationship between the data features and the different categories. The optimized vector is used as the weight vector to measure the contribution of the feature to the classification. According to the feature importance degree, the redundancy feature is gradually deleted, and the problem of selecting the key classification features of big data into the resolution principle is fused into the Henie theorem. The function limit and sequence limit of the key classification features of big data are obtained. Based on this, the key classification features of big data are selected. Experimental simulation shows that the proposed algorithm has higher classification accuracy and can effectively meet the needs of data analysis in the era of big data.

Lithology Classification Using TASI Thermal Infrared Hyperspectral Data with Convolutional Neural Networks

In recent decades, lithological mapping techniques using hyperspectral remotely sensed imagery have developed rapidly. The processing chains using visible-near infrared (VNIR) and shortwave infrared (SWIR) hyperspectral data are proven to be available in practice. The thermal infrared (TIR) portion of the electromagnetic spectrum has considerable potential for mineral and lithology mapping. In particular, the abovementioned rocks at wavelengths of 8–12 μm were found to be discriminative, which can be seen as a characteristic to apply to lithology classification. Moreover, it was found that most of the lithology mapping and classification for hyperspectral thermal infrared data are still carried out by traditional spectral matching methods, which are not very reliable due to the complex diversity of geological lithology. In recent years, deep learning has made great achievements in hyperspectral imagery classification feature extraction. It usually captures abstract features through a multilayer network, especially convolutional neural networks (CNNs), which have received more attention due to their unique advantages. Hence, in this paper, lithology classification with CNNs was tested on thermal infrared hyperspectral data using a Thermal Airborne Spectrographic Imager (TASI) at three small sites in Liuyuan, Gansu Province, China. Three different CNN algorithms, including one-dimensional CNN (1-D CNN), two-dimensional CNN (2-D CNN) and three-dimensional CNN (3-D CNN), were implemented and compared to the six relevant state-of-the-art methods. At the three sites, the maximum overall accuracy (OA) based on CNNs was 94.70%, 96.47% and 98.56%, representing improvements of 22.58%, 25.93% and 16.88% over the worst OA. Meanwhile, the average accuracy of all classes (AA) and kappa coefficient (kappa) value were consistent with the OA, which confirmed that the focal method effectively improved accuracy and outperformed other methods.

Cortical thickness distinguishes between major depression and schizophrenia in adolescents

Background Early diagnosis of adolescent psychiatric disorder is crucial for early intervention. However, there is extensive comorbidity between affective and psychotic disorders, which increases the difficulty of precise diagnoses among adolescents. Methods We obtained structural magnetic resonance imaging scans from 150 adolescents, including 67 and 47 patients with major depressive disorder (MDD) and schizophrenia (SCZ), as well as 34 healthy controls (HC) to explore whether psychiatric disorders could be identified using a machine learning technique. Specifically, we used the support vector machine and the leave-one-out cross-validation method to distinguish among adolescents with MDD and SCZ and healthy controls. Results We found that cortical thickness was a classification feature of a) MDD and HC with 79.21% accuracy where the temporal pole had the highest weight; b) SCZ and HC with 69.88% accuracy where the left superior temporal sulcus had the highest weight. Notably, adolescents with MDD and SCZ could be classified with 62.93% accuracy where the right pars triangularis had the highest weight. Conclusions Our findings suggest that cortical thickness may be a critical biological feature in the diagnosis of adolescent psychiatric disorders. These findings might be helpful to establish an early prediction model for adolescents to better diagnose psychiatric disorders.

Performance Evaluation of Machine Learning for Recognizing Human Facial Emotions

Facial expression recognition is a human emotion classification problem attracting much attention from scientific research. Classifying human emotions can be a challenging task for machines. However, more accurate results and less execution time are still the issues when extracting features of human emotions. To cope with these challenges, the authors propose an automatic system that provides users with a well-adopted classifier for recognizing facial expressions in a more accurate manner. The system is based on two fundamental machine learning stages, namely feature selection and feature classification. Feature selection is realized by active shape model (ASM) composed of landmarks while the feature classification algorithm is based on seven well-known classifiers. The authors have used CK+ dataset, implemented and tested seven classifiers to find the best classifier. The experimental results show that quadratic classifier (DA) provides excellent performance, and it outperforms the other classifiers with the highest recognition rate of 100% for the same dataset.

Classification & Feature extraction of Brain tumor from MRI Images using Modified ANN Approach

In this research paper, a new modified approach is proposed for brain tumor classification as well as feature extraction from Magnetic Resonance Imaging (MRI) after pre-processing of the images. The discrete wavelet transformation (DWT) technique is used for feature extraction from MRI images and Artificial Neural Network (ANN) is used for the classification of the type of tumor according to extracted features. Mean, Standard deviation, Variance, Entropy, Skewness, Homogeneity, Contrast, Correlation are the main features used to classify the type of tumor. The proposed model can give a better result in comparison with other available techniques in less computational time as well as a high degree of accuracy. The training and testing accuracies of the proposed model are 100% and 98.20% with a 98.70 % degree of precision respectively.