Human faces contain useful information that can be used in the identification of age, gender, weight etc. Among these biometrics, body mass index (BMI) and body weight are good indicators of a healthy person. Motivated by the recent health science studies, this work investigates ways to identify malnutrition affected people and obese people by analyzing body weight and BMI from facial images by proposing a regression method based on the 50‐layers Residual network architecture. For face detection, Multi‐task Cascaded Convolutional Neural Networks have been employed. A system is created to evaluate BMI along with age and gender from human facial real‐time images. Malnutrition and obesity are commonly determined with the help of BMI. In the previous works, height, weight, and BMI estimation through automatic means have predominantly focused on full‐body images and videos of humans. The usage of facial images for estimating such traits have been given less importance. In order to facilitate the analysis, the dataset is cleaned along with metadata containing information about the persons height, weight, age, and gender. Gender‐based analysis is performed for the prediction of BMI. Finally, an email containing the persons picture along with their details is sent to the concerned health officer.

In the processing of recognizing Φ-OTDR event signals by deep learning models, a large amount of annotated data is required for model training. However, the data annotation is a major challenge for a lot of unlabeled data and the labeling cost are high. This article proposes a valuable signal data selection method for Φ-OTDR sensing system. The data samples that are beneficial for training from the unlabeled data set is selected based on their uncertainty values. Through only labeling these valuable data samples and training the classification model with them, the labeling cost can be saved a lot and the classification model can keep its classification accuracy. The experiments show that this method can save 83.77% on manual labeling cost and the classification accuracy can still reach 96.37%, which is similar to the classification accuracy of the model trained by labeling all the data.

Strain and acceleration signals are essential for accurate event recognition along submarine cables. However, concise identification still poses challenges for the prompt recognition and classification of anchor smashing and hooking events with multiple types of sensors and multiple locations. For these reasons, this paper proposes a hybrid model that combines convolutional neural networks-bidirectional long short-term memory (CNN-BiLSTM) and a convolutional block attention module (CBAM) to instantaneously identify and organise anchor smashing and hooking events. Eight different categories of data were selected as data samples and collected from multiple Fiber Bragg Grating (FBG) strain sensors and accelerometers at four different locations. The results demonstrated that the recognition best accuracy of the method could reach 98.95%. This method has a better identification rate than the existing schemes used for the same purposes, confirming the validity and reliability of the proposed hybrid model.

This paper deals with uncertainty, asymmetric information, and risk modelling in a complex power system. The uncertainty is managed by using probability and decision theory methods. Multi-attribute decision-making (MADM) technique is a very effective and well-known tool to investigate fuzzy information more effectively. However, the selection of houses cannot be carried out by utilizing symmetry information, because enterprises does not have complete information, so asymmetric information should be used when selecting enterprises. Hamy mean (HM) operator is a feasible tool to handle strategic decision-making problems because it can capture the order between the finite input terms. Additionally, the complex interval-valued q-rung orthopair fuzzy (CIVq-ROF) setting is a broadly flexible and massively dominant technique to operate problematic and awkward data in actual life problems. The major contribution of this analysis is how to aggregate the collection of alternatives into a singleton set, for this we analyzed the technique of CIVq-ROF Hamy mean (CIVq-ROFHM) operator and CIVq-ROF weighted Hamy mean (Cq-ROFWHM) operator and some well-known results are deliberated. Keeping the advantages of the parameters in HM operators, we discussed the specific cases of the invented operators. To investigate the decision-making problems based on CIVq-ROF information, we suggested the following multi-attribute decision-making (MADM) technique to determine the beneficial term from the finite group of alternatives with the help of evaluating several examples. This manuscript showed how to make decisions when there is asymmetric information about enterprises. Finally, based on the evaluating examples, we try to discover the sensitive analysis and supremacy of the invented operators to find the flexibility and dominancy of the diagnosed approaches.

An efficient and robust meter readings location and recognition algorithm plays a very important role in remote meter reading systems and automatic meter inspection systems. A novel and efficient electric meter readings automatic location and recognition method is presented in this study. It includes the following three steps: coarse-to-fine detection of meter readings, projection-based character segmentation and BP neural network recognition. The algorithm was tested on HUALI, KEYUAN, JINLING, LANJIER and SANXING electric meter images. Experimental results show that the proposed method has satisfying performance and good robustness.