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The purpose of this study is to increase interest in health as human life is extended in modern society. Hence, many people in hospitals produce much medical data (EMR, PACS, OCS, EHR, MRI, X-ray) after treatment. Medical data are stored as structured and unstructured data. However, many medical data are causing errors, omissions and mistakes in th...
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... In view of this, this article proposes an intelligent inspection technology for protection devices with enhanced Faster R-CNN. Faster R-CNN are widely applied for image recognition, such as metal surface defects [10,11], medical detection [12,13], moving or fixed object recognition [14,15], and power equipment detection [16][17][18], which proves that Faster R-CNN can meet the intelligent supervision needs in complex scenarios. ...
Relay protection devices are necessary to guard the power system safety and stability. With the significant number of substations and relay protection devices, the maintenance workload has become difficult due to limited manpower, posing hidden dangers to the reliable operation of protection devices. In view of this, this article proposes an enhanced Faster R-CNN algorithm to diagnose the relay protection devices based on image monitoring. The proposed algorithm uses RestNet50 as the main tool to recognize features from input images to generate feature maps. Meanwhile, to improve the image detection accuracy, the Non-Maximum Sup-pression (NMS) algorithm in regional suggestion network (RPN) is optimized from solid thresh-old to soft threshold. By combining the images captured by the surveillance camera, intelligent inspections are conducted on the status of the pressing plate, fiber bending, external object retention, and other items in the relay protection device. A great number of samples are used to train networks for different tasks. The results indicate that the enhanced Faster R-CNN has a higher image recognition rate, and the recognition accuracy of each inspection item for the protection device is higher than 95%.
... In addition, such artificial intelligence is evolving from early artificial intelligence systems to machine learning and deep learning. This artificial intelligence technology is also being used in the medical field as shown in figure 4. (Kim and Huh, 2020) This information can be used to personalize treatment plans and implement preventive measures. AI-powered clinical decision support systems provide clinicians with real-time recommendations based on evidence-based guidelines, patient data, and medical literature. ...
The healthcare landscape in the United States is undergoing a transformative evolution fueled by rapid engineering innovations. This study synthesizes and examines key developments at the intersection of engineering and healthcare, emphasizing advancements that have significantly impacted medical practices, patient outcomes, and the overall healthcare ecosystem. The study outlines notable engineering applications, including medical imaging technologies, biomedical devices, telehealth solutions, and health information systems. It explores the integration of artificial intelligence and machine learning in healthcare engineering, emphasizing their role in diagnostics, personalized medicine, and treatment optimization. Additionally, the abstract discusses the implications of engineering innovations on healthcare accessibility, cost-effectiveness, and the potential to address emerging challenges. Through a comprehensive examination of developments in the USA, this study provides insights into the multifaceted role of engineering in shaping the present and future of healthcare delivery. Keywords: Engineering Innovations, Healthcare, USA, Review, Public Health, Development
... Most of the methods for enhancing heat transfer in existing heat exchanger systems are inclined towards better fluid mixing, thereby improving heat transfer efficiency in different types of applications like conversion of liquid to vapour [1,2] and low droplet impact cooling [3][4][5]. The various passive heat transfer enhancement methods are ribs and impingement [6,7], vortex generators [8], the usage of numerous microchannels [9,10], small pin fins [11] and conventional twisted tapes [12]. These techniques cause rapid fluid mixing between cold and hot regions in the flow sections, further causing higher heat transfer. ...
... The terms f c and f a are the equivalent smooth-tube friction factor and enhanced-case friction factor at the same pressure drop, i.e., equal pumping power. Using the Blasius equation [27], the friction factor for smooth-tube turbulent flow conditions, f c = 0.079 Re c 0.25 (9) and Equation (8) gives ...
Heat transfer enhancement using curved ribs of different cross sections, viz., square, rectangular, triangular, and circular, is a crucial study for designing heat-exchanging devices for various applications, and their thermohydraulic performance prediction using machine learning technique is a vital part of the modern world. An experimental study on using curved ribs suitable for heat transfer enhancement for the circular tube is presented for turbulent airflow with Reynolds numbers varying from 10,000 to 50,000. The machine learning methodology is used to predict the thermohydraulic performance assessment of curved ribs. The square cross-sectioned curved ribs produce the highest performance factor R3 of 1.5 to 2.65 to the equivalent Reynolds number Rec value of 20,000. It is observed that most of the curved rib configurations show a performance ratio R3 maximum and are suitable at a low Reynolds number value. At moderate and high Reynolds number values, the performance factor values decrease due to a rise in the pressure drop values for a few curved rib configurations. An artificial neural network (ANN) model predicts with an accuracy of 95% with the present study experimental values for the heat transfer performance indicators like average heat transfer enhancement Nua/Nus, average heat transfer enhancement fa/fs, and performance ratio R3, i.e., Nua/Nuc.
... Moreover, artificial intelligence (AI) technology based on deep learning has been rapidly advanced and widely applied along with machine vision in the fishing industry [14][15]. Particularly, AI combined with machine vision brings powerful synergy effects [16][17][18][19][20][21][22]. Since AI machine vision exhibits exceptional performance, it is expected that this technology will be effectively applied in various industries including the fishing industry. ...
Food processing companies pursue the distribution of ingredients that were packaged according to a certain weight. Particularly, foods like fish are highly demanded and supplied. However, despite the high quantity of fish to be supplied, most seafood processing companies have yet to install automation equipment. Such absence of automation equipment for seafood processing incurs a considerable cost regarding labor force, economy, and time. Moreover, workers responsible for fish processing are exposed to risks because fish processing tasks require the use of dangerous tools, such as power saws or knives. To solve these problems observed in the fish processing field, this study proposed a fish cutting point prediction method based on AI machine vision and target weight. The proposed method performs three-dimensional (3D) modeling of a fish’s form based on image processing techniques and partitioned random sample consensus (RANSAC) and extracts 3D feature information. Then, it generates a neural network model for predicting fish cutting points according to the target weight by performing machine learning of the extracted 3D feature information and measured weight information. This study allows for the direct cutting of fish based on cutting points predicted by the proposed method. Subsequently, we compared the measured weight of the cut pieces with the target weight. The comparison result verified that the proposed method showed a mean error rate of approximately 3%.
... For example, if a query word inputted by a user is court, the search engine should present the results by categorizing the information into courthouse-related and palace-related suggestions. In addition, it is important to resolve semantic ambiguity in text mining for documents in specialized fields such as medical documents [2,3]. Lexical disambiguation has been a primary interest since the 1950s when natural languages began to be processed by computers. ...
... The improved method for lexical disambiguation in this paper solves the data proficiency problem as follows: 1 A weight is adjusted according to the types of semantically related words of an ambiguous word so that more information regarding the relation words can be used than in existing methods. 2 Semantically related words of an ambiguous word and the coordinate terms of the related words are expanded so that more information can be used than in existing methods. 3 Using the part-of-speech information of words, normalization is done for words such as numerals and proper nouns. Table 7 shows a comparison of the performance between the basic and improved algorithms. ...
... Using statistical information extracted from the learning corpus, a primary model was constructed (using related words and relation words) and 2 using the primary model, lexical disambiguation was conducted with regard to learning corpus. 3 Prior knowledge was extracted using the lexical disambiguation result, 4 . A secondary model was constructed using the primary model and extracted prior knowledge and 5 lexical disambiguation was conducted with regard to the evaluation of the corpus using the secondary model. ...
Supervised disambiguation using a large amount of corpus data delivers better performance than other word sense disambiguation methods. However, it is not easy to construct large-scale, sense-tagged corpora since this requires high cost and time. On the other hand, implementing unsupervised disambiguation is relatively easy, although most of the efforts have not been satisfactory. A primary reason for the performance degradation of unsupervised disambiguation is that the semantic occurrence probability of ambiguous words is not available. Hence, a data deficiency problem occurs while determining the dependency between words. This paper proposes an unsupervised disambiguation method using a prior probability estimation based on the Korean WordNet. This performs better than supervised disambiguation. In the Korean WordNet, all the words have similar semantic characteristics to their related words. Thus, it is assumed that the dependency between words is the same as the dependency between their related words. This resolves the data deficiency problem by determining the dependency between words by calculating the χ2 statistic between related words. Moreover, in order to have the same effect as using the semantic occurrence probability as prior probability, which is used in supervised disambiguation, semantically related words of ambiguous vocabulary are obtained and utilized as prior probability data. An experiment was conducted with Korean, English, and Chinese to evaluate the performance of our proposed lexical disambiguation method. We found that our proposed method had better performance than supervised disambiguation methods even though our method is based on unsupervised disambiguation (using a knowledge-based approach).
... Speech processing technology has demonstrated great potential to provide beneficial solutions for both patients and doctors in smart healthcare. Recent advances in speech processing technology and other advanced technologies, including the Internet of Things (IoT) and communication systems, have significantly advanced contemporary healthcare systems [1][2][3]. In particular, recent innovations in deep learning, the advent of IoT and new communication systems have opened up various possibilities for medical systems. ...
... The method proposed adopts a simple but more appropriate prosody labeling system and training procedure for such labelers. (2) The predicted locations of mandatory prosodic breaks are processed with partial parsing analysis of syntactic structures. Based on this, rules are established that predict mandatory prosodic breaks. ...
Speech processing technology has great potential in the medical field to provide beneficial solutions for both patients and doctors. Speech interfaces, represented by speech synthesis and speech recognition, can be used to transcribe medical documents, control medical devices, correct speech and hearing impairments, and assist the visually impaired. However, it is essential to predict prosody phrase boundaries for accurate natural speech synthesis. This study proposes a method to build a reliable learning corpus to train prosody boundary prediction models based on deep learning. In addition, we offer a way to generate a rule-based model that can predict the prosody boundary from the constructed corpus and use the result to train a deep learning-based model. As a result, we have built a coherent corpus, even though many workers have participated in its development. The estimated pairwise agreement of corpus annotations is between 0.7477 and 0.7916 and kappa coefficient (K) between 0.7057 and 0.7569. In addition, the deep learning-based model based on the rules obtained from the corpus showed a prediction accuracy of 78.57% for the three-level prosody phrase boundary, 87.33% for the two-level prosody phrase boundary.
... Therefore, deep CNNs have been widely used in many fields. [28][29][30][31][32][33] Zhang et al. 34 proposed a bolt looseness damage detection method based on the Faster Region-Convolutional Neural Network (Faster R-CNN), which can automatically identify bolt damage states. Wang et al. 35 used Faster R-CNN and You Only Look Once (YOLO) to detect bolt. ...
With the development of deep learning, object detection algorithms based on horizontal box are widely used in the field of damage identification. However, damages can be in any direction and position, and they are not necessarily horizontal or vertical. This article proposes a bolt damage identification network, namely, orientation-aware center point estimation network, which models a damage as a center point of its rotated bounding box. The proposed orientation-aware center point estimation network uses deep layer aggregation network to search center points and regress to all other damage properties, such as size and angle. A loss function is designed to improve the optimization efficiency of network. Orientation-aware center point estimation network is applied to bolt damage detection, and comparison with the well-known Faster Region-Convolutional Neural Network (a benchmark using horizontal bounding box) demonstrates the accuracy of the proposed method. Finally, videos were utilized to verify the capability of the proposed orientation-aware center point estimation network in real-time detection of bolt damages.
... The advancement of AI technology provides an efficient tool to automate or assist in the diagnosis of pathology and to improve the current dilemma of the lack of pathologists. AI models in digital pathology have evolved from expert systems to traditional machine learning (ML) to DL (35,36). Both expert systems and traditional ML models rely on the rules or features defined by experts on the basis of their experience. ...
Background and objective:
Pathology is the gold standard criteria for breast cancer diagnosis and has important guiding value in formulating the clinical treatment plan and predicting the prognosis. However, traditional microscopic examinations of tissue sections are time consuming and labor intensive, with unavoidable subjective variations. Deep learning (DL) can evaluate and extract the most important information from images with less need for human instruction, providing a promising approach to assist in the pathological diagnosis of breast cancer. To provide an informative and up-to-date summary on the topic of DL-based diagnostic systems for breast cancer pathology image analysis and discuss the advantages and challenges to the routine clinical application of digital pathology.
Methods:
A PubMed search with keywords ("breast neoplasm" or "breast cancer") and ("pathology" or "histopathology") and ("artificial intelligence" or "deep learning") was conducted. Relevant publications in English published from January 2000 to October 2021 were screened manually for their title, abstract, and even full text to determine their true relevance. References from the searched articles and other supplementary articles were also studied.
Key content and findings:
DL-based computerized image analysis has obtained impressive achievements in breast cancer pathology diagnosis, classification, grading, staging, and prognostic prediction, providing powerful methods for faster, more reproducible, and more precise diagnoses. However, all artificial intelligence (AI)-assisted pathology diagnostic models are still in the experimental stage. Improving their economic efficiency and clinical adaptability are still required to be developed as the focus of further researches.
Conclusions:
Having searched PubMed and other databases and summarized the application of DL-based AI models in breast cancer pathology, we conclude that DL is undoubtedly a promising tool for assisting pathologists in routines, but further studies are needed to realize the digitization and automation of clinical pathology.
Micro-pin fin heat sinks are receiving attention for their use in the thermal management of high-heat-flux electronics systems since they can help to enhance heat transfer characteristics (owing to their large extended surface area) and flow mixing while requiring relatively low pumping power compared with conventional microchannel heat sinks. Although many studies have determined the thermal performance of micro-pin fin heat sinks over the past several decades, a universal model for predicting the thermal performance of micro-pin fin heat sinks with various geometries and under different operating conditions has not been developed. In this study, we developed universal machine learning models for predicting the thermal performance of micro-pin fin heat sinks of various shapes and under different operating conditions beyond the limits of existing correlations by using power law regression. The database for these models comprised 906 data points amassed from 15 studies. Three machine learning models and a newly proposed regression model were compared with the conventional regression models. The prediction accuracies of each model and complex relations between the geometric shape, operating conditions, and heat transfer performance are discussed by comparing the three machine learning models and the regression model. The machine learning models had mean absolute errors (MAEs) of 7.5–10.9%, representing an approximately fivefold enhancement in the prediction accuracy compared with existing regression correlations. Their MAEs were lower than that of the regression model. Moreover, the machine learning models provided high accuracies for rare geometric shapes and operating conditions, such as a triangular pin shape or the use of R134A as a working fluid. These results showed the superiority of the machine learning models over traditional correlations in terms of the prediction accuracy for the thermal performance of micro-pin fin heat sinks over a wide range of geometric and operating conditions.
