Smart system for maintaining aquascape environment using internet of things based light and temperature controller

<span>The purpose of this research is to create a smart system based on internet of things (IoT) application for a plant aquarium. This smart system helps users to maintain the environment's parameters of the plant aquarium. In this study, the parameters to be controlled by the system are light intensity and temperature. The hardware used to develop this system is the ESP32 as the microcontroller, BH1750FVI as the light sensor, high power led (HPL) light-emitting diodes (LED) lamp as the light source, DS18B20 as temperature sensor, the heater, and the 220 VAC fan that is used to raise and lower the temperature. In this study also developed an application that is used by the user to provide input to the system. The developed application is then installed on the user's smartphone and used to connect the user to the system via the internet. The ease of adding and removing devices used on the system is a capability that is also being developed in this smart system. The developed system can produce light intensity with accuracy rate of 96% and always manage to keep the temperature within the predetermined range.</span>

A New Concept of Electronic Text Based on Semantic Coding System for Machine Translation

In the field of machine translation of texts, the ambiguity in both lexical (dictionary) and structural aspects is still one of the difficult problems. Researchers in this field use different approaches, the most important of which is machine learning in its various types. The goal of the approach that we propose in this article is to define a new concept of electronic text, which makes the electronic text free from any lexical or structural ambiguity. We used a semantic coding system that relies on attaching the original electronic text (via the text editor interface) with the meanings intended by the author. The author defines the meaning desired for each word that can be a source of ambiguity. The proposed approach in this article can be used with any type of electronic text (text processing applications, web pages, email text, etc.). Thanks to the approach that we propose and through the experiments that we have conducted using it, we can obtain a very high accuracy rate. We can say that the problem of lexical and structural ambiguity can be completely solved. With this new concept of electronic text, the text file contains not only the text but also with it the true sense of the exact meaning intended by the writer in the form of symbols. These semantic symbols are used during machine translation to obtain a translated text completely free of any lexical and structural ambiguity.

Automatic Receipt Recognition System Based on Artificial Intelligence Technology

In this study, an automatic receipt recognition system (ARRS) is developed. First, a receipt is scanned for conversion into a high-resolution image. Receipt characters are automatically placed into two categories according to the receipt characteristics: printed and handwritten characters. Images of receipts with these characters are preprocessed separately. For handwritten characters, template matching and the fixed features of the receipts are used for text positioning, and projection is applied for character segmentation. Finally, a convolutional neural network is used for character recognition. For printed characters, a modified You Only Look Once (version 4) model (YOLOv4-s) executes precise text positioning and character recognition. The proposed YOLOv4-s model reduces downsampling, thereby enhancing small-object recognition. Finally, the system produces recognition results in a tax declaration format, which can upload to a tax declaration system. Experimental results revealed that the recognition accuracy of the proposed system was 80.93% for handwritten characters. Moreover, the YOLOv4-s model had a 99.39% accuracy rate for printed characters; only 33 characters were misjudged. The recognition accuracy of the YOLOv4-s model was higher than that of the traditional YOLOv4 model by 20.57%. Therefore, the proposed ARRS can considerably improve the efficiency of tax declaration, reduce labor costs, and simplify operating procedures.

Prediction of Osteoporosis Through Deep Learning Algorithms on Panoramic Radiographs

Objective: The aim of the present study was to predict osteoporosis on panoramic radiographs of women over 50 years of age through deep learning algorithms.Method: Panoramic radiographs of 744 female patients over 50 years of age were labeled as C1, C2, and C3 depending on mandibular cortical index (MCI). According to this index; C1: presence of a smooth and sharp mandibular cortex (normal); C2: resorption cavities at endosteal margin and 1 to 3-layer stratification (osteopenia); C3: completely porotic cortex (osteoporosis). The data of the present study were reviewed in different categories including C1-C2-C3, C1-C2, C1-C3 and C1-(C2+C3) as two-class and three-class prediction. The data were separated as 20% random test data; and the remaining data were used for training and validation with 5-fold cross-validation. AlexNET, GoogleNET, ResNET-50, SqueezeNET, and ShuffleNET deep learning models are trained through the transfer learning method. The results were evaluated by performance criteria including accuracy, sensitivity, specificity, F1-score, AUC and training duration. Findings: The dataset C1-C2-C3 has an accuracy rate of 81.14% with AlexNET; the dataset C1-C2 has an accuracy rate of 88.94% with GoogleNET; the dataset C1-C3 has an accuracy rate of 98.56% with AlexNET; and the dataset C1-(C2+C3) has an accuracy rate of 92.79% with GoogleNET. Conclusion: The highest accuracy was obtained in differentiation of C3 and C1 where osseous structure characteristics change significantly. Since the C2 score represent the intermediate stage (osteopenia), structural characteristics of the bone present behaviors closer to C1 and C3 scores. Therefore, the data set including the C2 score provided relatively lower accuracy results.

Factors Associated With the Accuracy of Depth Gauge Measurements

Background: It is important to select appropriate screws in orthopedic surgeries, as excessively long or too short a screw may results failure of the surgeries. This study explored factors that affect the accuracy of measurements in terms of the experience of the surgeons, passage of drilled holes and different depth gauges.Methods: Holes were drilled into fresh porcine femurs with skin in three passages, straight drilling through the metaphysis, straight drilling through the diaphysis, and angled drilling through the diaphysis. Surgeons with different surgical experiences measured the holes with the same depth gauge and using a vernier caliper as gold standard. The length of selected screws, and the time each surgeon spent were recorded. The measurement accuracy was compared based on the experiences of the surgeons and the passage of drilled holes. Further, parameters of depth gauges and 12-mm cortical bone screws from five different manufacturers were measured.Results: A total of 13 surgeons participated in 585 measurements in this study, and each surgeon completed 45 measurements. For the surgeons in the senior, intermediate, and junior groups, the average time spent in measurements was 689, 833, and 785 s with an accuracy of 57.0, 42.2, and 31.5%, respectively. The accuracy and measurement efficiency were significantly different among the groups of surgeons (P &lt; 0.001). The accuracy of measurements was 45.1% for straight metaphyseal drilling, 43.6% for straight diaphyseal drilling, and 33.3% for angled diaphyseal drilling (P = 0.036). Parameters of depth gauges and screws varied among different manufacturers.Conclusion: Both observer factor and objective factors could affect the accuracy of depth gauge measurement. Increased surgeon's experience was associated with improvements in the accuracy rate and measurement efficiency of drilled holes based on the depth gauge. The accuracy rate varied with hole passages, being the lowest for angled drilled holes.

Application and Analysis of Education and Teaching Mode Based on 5G and Smart Technology

The development of information technology has brought tremendous changes to our country’s education. Based on the 5G + Internet, the article proposes a brand-new intelligent education model and proposes an ST analysis method, which mainly studies the teacher and students in the classroom. Class performance, based on Yebes network, proposed a learning decision method and application. The research results of the article show the following: (1) The article compares the monthly test scores of three variables under two different teaching modes. The results show that the performance of the online teaching mode is generally better than that of the traditional teaching mode, and the performance of the experimental class has increased more, with an average growth score of 4.83, indicating that there are significant differences in students’ learning and cognitive abilities under different teaching models. (2) The article compares the students’ knowledge mastery under two different teaching modes. The results show that under the traditional teaching mode, the students’ knowledge mastery is low, and the complete mastery rate is only 15%. In the network multimedia teaching mode, the students’ knowledge mastery rate has been greatly improved, the complete mastery rate is as high as 45%, and the students’ mastery of knowledge has been extremely improved, indicating that the network multimedia teaching mode can stimulate students’ learning interest more, improve learning efficiency. (3) Studying the differences in the source of curriculum resources of three different types of teachers, and the results show that the proportion of curriculum resources downloaded through the Internet is the largest; in the investigation of the impact of multimedia teaching on the classroom, the cooperation rate of students when multimedia teaching is not used, classroom practice accuracy and classroom completion rate are low, but after using multimedia teaching, students’ cooperation rate and classroom practice accuracy rate have been greatly improved, among which the accuracy rate of the experimental class is as high as 62.4%, and the students’ thinking ability is also good. Great improvement.

Toward Automatic Detection of Gastric Lesion for Upper Gastrointestinal Endoscopy with Neural Network

In this paper, we compared the performance of several neural networks in the classification of early gastric cancer (EGC) images and proposed a method of converting the output value of the network into a calorific value to locate the lesion. The algorithm was improved using transfer learning and fine-tuning principles. The test set accuracy rate reached 0.72, sensitivity reached 0.67, specificity reached 0.77, and precision rate reached 0.78. The experimental results show the potential to meet clinical demands for automatic detection of gastric lesion.

Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation

This paper aims at ensuring an efficient recommendation. It proposes a new context-aware semantic-based probabilistic situations injection and adaptation using an ontology approach and Bayesian-classifier. The idea is to predict the relevant situations for recommending the right services. Indeed, situations are correlated with the user’s context. It can, therefore, be considered in designing a recommendation approach to enhance the relevancy by reducing the execution time. The proposed solution in which four probability-based-context rule situation items (user’s location and time, user’s role, their preferences and experiences) are chosen as inputs to predict user’s situations. Subsequently, the weighted linear combination is applied to calculate the similarity of rule items. The higher scores between the selected items are used to identify the relevant user’s situations. Three context parameters (CPU speed, sensor availability and RAM size) of the current devices are used to ensure adaptive service recommendation. Experimental results show that the proposed approach enhances accuracy rate with a high number of situations rules. A comparison with existing recommendation approaches shows that the proposed approach is more efficient and decreases the execution time.

Underwater Fish Detection and Counting Using Mask Regional Convolutional Neural Network

Fish production has become a roadblock to the development of fish farming, and one of the issues encountered throughout the hatching process is the counting procedure. Previous research has mainly depended on the use of non-machine learning-based and machine learning-based counting methods and so was unable to provide precise results. In this work, we used a robotic eye camera to capture shrimp photos on a shrimp farm to train the model. The image data were classified into three categories based on the density of shrimps: low density, medium density, and high density. We used the parameter calibration strategy to discover the appropriate parameters and provided an improved Mask Regional Convolutional Neural Network (Mask R-CNN) model. As a result, the enhanced Mask R-CNN model can reach an accuracy rate of up to 97.48%.

A Deep Neural Network-Based Target Recognition Algorithm for Robot Scenes

Intelligent robots are a key vehicle for artificial intelligence and are widely employed in all aspects of everyday life and work, not just in the industry. One of the talents required for intelligent robots to complete their jobs is the capacity to identify their environment, which is a crucial obstacle to be overcome. Deep learning-based target identification algorithms currently do not fully leverage the link between high-level semantic and low-level detail information in the prediction step and hence are less successful in recognizing tiny target objects. Target recognition via vision sensors has also improved in accuracy and efficiency because of the development of deep learning. However, due to the insufficient usage of semantic information and precise texture information of underlying characteristics, tiny target recognition remains a difficulty. To address the aforementioned issues, we propose a target detection method based on a jump-connected pyramid model to improve the target detection performance of robots in complex scenarios. In order to verify the effectiveness of the algorithm, we designed and implemented a software system for target detection of intelligent robots and performed software integration of the proposed algorithm model with excellent experimental results. These experiments reveal that, when compared to other algorithms, our suggested algorithm’s characteristics have higher flexibility and robustness and can deliver a higher scene classification accuracy rate.