Development Of Artificial Intelligence Methods Research Articles

Assets Performance Management systems for hydroelectric power plants—A survey

This paper presents a survey about several works in the field of predictive maintenance for hydroelectric power plants related to Assets Performance Management systems. It discusses about how to make a system study to better understand the structure of the Hydropower Plants and its components and how to define the most significant items to the maintenance. Also, it presents predictive maintenance techniques in terms of failure detection, diagnosis and prognostics. The research shows that data-driven methods are increasingly used by Hydropower Plants. With the recent development of artificial intelligence methods, it is possible to discover unknown patterns in sensor data and learn existing relationships in the data that may not be easy for a human to discover. In this way, some hybrid methods, which mix concepts such as the transformation of time series data into the frequency domain, statistical modeling and machine learning, are being developed to perform the tasks of APM systems. The work also shows the possibility of developing methods focusing on reinforcement learning or explainable artificial intelligence for the industrial sector. Such advances can count as a contribution to the innovative nature of the electricity sector, which tends to become increasingly sustainable in order to mitigate the environmental impacts caused by production.

Artificial Intelligence Technologies in the Microsurgical Operating Room (Review).

Surgery performed by a novice neurosurgeon under constant supervision of a senior surgeon with the experience of thousands of operations, able to handle any intraoperative complications and predict them in advance, and never getting tired, is currently an elusive dream, but can become a reality with the development of artificial intelligence methods. This paper has presented a review of the literature on the use of artificial intelligence technologies in the microsurgical operating room. Searching for sources was carried out in the PubMed text database of medical and biological publications. The key words used were "surgical procedures", "dexterity", "microsurgery" AND "artificial intelligence" OR "machine learning" OR "neural networks". Articles in English and Russian were considered with no limitation to publication date. The main directions of research on the use of artificial intelligence technologies in the microsurgical operating room have been highlighted. Despite the fact that in recent years machine learning has been increasingly introduced into the medical field, a small number of studies related to the problem of interest have been published, and their results have not proved to be of practical use yet. However, the social significance of this direction is an important argument for its development.

Applications of Artificial Intelligence in Neonatology

The development of artificial intelligence methods has impacted therapeutics, personalized diagnostics, drug discovery, and medical imaging. Although, in many situations, AI clinical decision-support tools may seem superior to rule-based tools, their use may result in additional challenges. Examples include the paucity of large datasets and the presence of unbalanced data (i.e., due to the low occurrence of adverse outcomes), as often seen in neonatal medicine. The most recent and impactful applications of AI in neonatal medicine are discussed in this review, highlighting future research directions relating to the neonatal population. Current AI applications tested in neonatology include tools for vital signs monitoring, disease prediction (respiratory distress syndrome, bronchopulmonary dysplasia, apnea of prematurity) and risk stratification (retinopathy of prematurity, intestinal perforation, jaundice), neurological diagnostic and prognostic support (electroencephalograms, sleep stage classification, neuroimaging), and novel image recognition technologies, which are particularly useful for prompt recognition of infections. To have these kinds of tools helping neonatologists in daily clinical practice could be something extremely revolutionary in the next future. On the other hand, it is important to recognize the limitations of AI to ensure the proper use of this technology.

An Intelligent Edge-as-a-Service Framework to Combat COVID-19 Using Deep Learning Techniques

This study proposes and develops a secured edge-assisted deep learning (DL)-based automatic COVID-19 detection framework that utilizes the cloud and edge computing assistance as a service with a 5G network and blockchain technologies. The development of artificial intelligence methods through services at the edge plays a significant role in serving many applications in different domains. Recently, some DL approaches have been proposed to successfully detect COVID-19 by analyzing chest X-ray (CXR) images in the cloud and edge computing environments. However, the existing DL methods leverage only local and small training datasets. To overcome these limitations, we employed the edges to perform three tasks. The first task was to collect data from different hospitals and send them to a global cloud to train a DL model on massive datasets. The second task was to integrate all the trained models on the cloud to detect COVID-19 cases automatically. The third task was to retrain the trained model on specific COVID-19 data locally at hospitals to improve and generalize the trained model. A feature-level fusion and reduction were adopted for model performance enhancement. Experimental results on a public CXR dataset demonstrated an improvement against recent related work, achieving the quality-of-service requirements.

System of Operations on Associative Heterarchical Memory

Text processing in natural language remains an important task for the field of development of artificial intelligence methods and tools. Since the twentieth century, artificial intelligence methods have been divided into two paradigms — top-down and bottom-up. The methods of the ascending paradigm are difficult to interpret in the form of the output of natural language, and the methods of the descending paradigm are difficult to actualize information. Taking into account the authors approach to the construction of artificial intelligence agents, the processing of natural language must be performed on two levels: on the lower level, using methods of the bottom-up paradigm, and on the upper level, using symbolic methods of the top-down paradigm. The authors of the article have already introduced a new mathematical formalism based on the notion of a hypergraph — associative heterarchical memory (AH-memory). Such memory should simplify the process of natural language processing with new technologies. Earlier the authors group has thoroughly analyzed the problem of symbol binding in the application to АН-memory and its structure. In the first paper, abstract symbol binding was performed using multi-serial integration, eventually converting the primary symbols received by the program into integrated abstract symbols. The second paper provided a comprehensive description of the AH-memory in the form of formulas, explanations of them, and their corresponding diagrams. Although there are many possible modules to use, the developer working with AH-memory should choose those parts of AH-memory which are required for successful and efficient functioning of the AI agent. The article will be of interest to developers of artificial intelligence methods and tools, mathematicians and specialists in natural language processing.

A data-driven explainable case-based reasoning approach for financial risk detection

The rapid development of artificial intelligence methods contributes to their wide applications for forecasting various financial risks in recent years. This study introduces a novel explainable case-based reasoning (CBR) approach without a requirement of rich expertise in financial risk. Compared with other black-box algorithms, the explainable CBR system allows a natural economic interpretation of results. Indeed, the empirical results emphasize the interpretability of the CBR system in predicting financial risk, which is essential for both financial companies and their customers. In addition, our results show that the proposed automatic design CBR system has a good prediction performance compared to other artificial intelligence methods, overcoming the main drawback of a standard CBR system of highly depending on prior domain knowledge about the corresponding field.

VAPER: A deep learning model for explainable probabilistic regression

A probabilistic regression model provides decision-makers with the regression output along with its quantitative uncertainty for given input variables. Even though this uncertainty may help to avoid serious consequences such as misdiagnosis or blackout due to overconfidence in the output, it only provides a measure of the uncertainty of the output, and has a limitation in that it cannot explain the reasons for the output and its uncertainty. If they can be presented along with their reasons, more suitable alternatives to the output can be found. However, despite the development of artificial intelligence methods to explain machine learning models and their outputs, few probabilistic regression models with this functionality have been proposed. Therefore, in this paper, we propose a variational autoencoder-based model for explainable probabilistic regression, called VAPER. VAPER provides a parametric probability distribution of an output variable over input variables and interprets it using layer-wise relevance propagation to investigate the effect of each input variable. To evaluate the effectiveness of the proposed model, we performed extensive experiments using several datasets. The experimental results demonstrated that VAPER has competitive regression performance compared to existing models, even with effective explainability.

Development of Artificial Intelligence Methods for Determination of Methane Solubility in Aqueous Systems

Accurate determinations of water (H2O) content in natural gases especially in the methane (CH4) phase are highly important for chemical engineers dealing with natural gas processes. To this end, development of a high performance model is necessary. Due to importance of the solubility of methane in the aqueous solutions for natural gas industries, two novel models based on the Decision Tree (DT) and Adaptive Neuro-Fuzzy Interference System (ANFIS) have been employed. To this end, a total number of 204 real methane solubility points in aqueous solution containing NaCl under different pressure and temperature conditions have been gathered. The comparisons between predicted solubility values and experimental data points have been conducted in visual and mathematical approaches. The R2 values of 1 for training and testing phases express the great ability of proposed models in calculation of methane solubility in pure water systems.

Possibility of applying geoinformation multiagent optimisation for planning the development of road networks

Abstract In recent years, computational intelligence has been used to solve optimisation problems. An innovative direction in the development of artificial intelligence methods is multiagent methods of intellectual optimisation, which simulate the collective behaviour of insects, animals and other living beings. It indicates the effectiveness of their behaviour, and hence the effectiveness of these methods, and the ability to be involved in solving applied problems. This article is devoted to the study of the development of road transport networks using the metaheuristic ant method of optimisation based on a number of data. The initial data were geospatial layers of information on slope steepness, engineering structures, forests, perennials, land development and hydrographic objects. The parameters of the behaviour of the studied method under different conditions and volumes of input geospatial data are experimentally established. The Max–Min method of multiagent optimisation is modified. The proposed modification takes into account the functional distance – the coefficient of the complexity of the route, which affects its length. This modification had an effective influence on the behaviour of ants and the choice of optimal routes, taking into account the terrain as one of the factors. The result of the advancement is an informational system, which is capable of formulating flexible options for passing optimal alternative routes between specified settlements.

A Data-driven Explainable Case-based Reasoning Approach for Financial Risk Detection

The rapid development of artificial intelligence methods contributes to their wide applications for forecasting various financial risks in recent years. This study introduces a novel explainable case-based reasoning (CBR) approach without a requirement of rich expertise in financial risk. Compared with other black-box algorithms, the explainable CBR system allows a natural economic interpretation of results. Indeed, the empirical results emphasize the interpretability of the CBR system in predicting financial risk, which is essential for both financial companies and their customers. In addition, our results show that the proposed automatic design CBR system has a good prediction performance compared to other artificial intelligence methods, overcoming the main drawback of a standard CBR system of highly depending on prior domain knowledge about the corresponding field.

Cell‐type based semantic segmentation of histopathological images using deep convolutional neural networks

AbstractHistopathological whole‐slide image (WSI) analysis is still one of the most important ways to identify regions of cancer risk. For cancer in which early diagnosis is vital, pathologists are at the center of the decision‐making process. Thanks to the widespread use of digital pathology and the development of artificial intelligence methods, automatic histopathological image analysis methods help pathologists in their decision‐making process. In this process, rather than producing labels for whole‐slide image patches, semantic segmentation is very useful, which facilitates the pathologists’ interpretation. In this study, automatic semantic segmentation based on cell type is proposed for the first time in the literature using novel deep convolutional networks structure (DCNN). We presents semantic information on four classes, including white areas in the whole‐slide image, tissue without cells, tissue with normal cells and tissue with cancerous cells. This visual information presented to the pathologist is an easy‐to‐understand picture of the status of the cells and their implications for the spread of cancerous cells. A new DCNN architecture is created, inspired by the residual network and deconvolution network architecture. Our network is trained end‐to‐end manner with histopathological image patches for cell structures to be more discriminative. The proposed method not only produces more successful results than other state‐of‐art semantic segmentation algorithms with 9.2% training error and 88.89% F‐score for test, but also has the most important advantage in that it has the ability to generate automatic information about the cancer and also provides information that pathologists can quickly interpret.

Wireless Sensor Networks: Toward Smarter Railway Stations

Railway industry plays a critical role in transportation and transit systems attributed to the ever-growing demand for catering to both freight and passengers. However, owing to many challenges faced by railway stations such as harsh environments, traffic flow, safety and security risks, new and adaptive systems employing new technology are recommended. In this review, several wireless sensor networks (WSNs) applications are proposed for use in railway station systems, including advanced WSNs, which will enhance security, safety, and decision-making processes to achieve more cost-effective management in railway stations, as well as the development of integrated systems. The size, efficiency, and cost of WSNs are influential factors that attract the railway industry to adopt these devices. This paper presents a review of WSNs that have been designed for uses in monitoring and securing railway stations. This article will first briefly focus on the presence of different WSN applications in diverse applications. In addition, it is important to note that exploitation of the state-of-the-art tools and techniques such as WSNs to gain an enormous amount of data from a railway station is a new and novel concept requiring the development of artificial intelligence methods, such machine learning, which will be vital for the future of the railway industry.

Development of expert system prototype for flexible reorientation women’s outerwear production

The garment industry quickly becomes a highly developed branch due to the rapid development of technologies that contribute to high-quality design, cutting, manufacture. However, some design stages have not yet been formalized. For solving unformalized tasks, the expert systems are used. The research deals with developing the expert system prototype for rapid reorientation of women’s outerwear production. To form a subject environment, the textual method is used. Factor and cluster analyses are used to structure the subject environment. Thus, the main objective of the study is achieved by forming twelve individual tasks according to the number of individual groups, allocated in the subject environment of rapid reorientation of women’s outerwear production. Selection rules of transformation chain and values of additions at the level of chest, waist and hips are formed in tables. In each table, results are obtained at the intersection of several conditions.The expert system prototype for flexible reorientation of women’s outerwear production is designed by using the empty expert system “Rapana”. The expert system prototype implements a dialogue with the user as a series of questions and answers of the user. Some answers can have a degree of confidence. The user can revise the way of decision-making after obtaining the results. Thus, necessary conditions for further development of artificial intelligence methods in the garment production design training management and for reducing risks of wrong decision-making in conditions of rapid change in project situations are created

Flow cytometry in diagnostic cytology.

Flow cytometry (FCM) is a useful adjunct to cytologic examination, because the quantitative biochemical information it provides complements the morphologic information gained during visual examination. It aids in the interpretation of bladder washings, and is particularly useful for the assessment of lymphoid lesions, whether they originate from fine-needle aspiration, cerebrospinal fluid, or effusions. Optimal use of FCM frequently requires assessment of more than one parameter; simultaneous use of cell differentiation markers and nuclear DNA quantitation is often significantly more useful than either alone. Despite the utility of FCM, however, the potential for future development appears to be limited. Improvements in image cytometry allow reasonable assessment of ploidy and S-fraction to be made from specimens prepared on glass slides. Multiparameter measurements may also be accomplished with imaging techniques, which allow the further advantage of visual identification of cells with equivocal morphologic changes. The development of artificial intelligence methods for use with imaging technology has also significantly exceeded that of FCM. Finally, image cytometry is often more useful for samples with few cells. Other challenges are posed by immunocytochemical methods which compete with flow cytometry as tools for assessment of proliferation. Given the relatively high cost of FCM instrumentation, survival of FCM as an ancillary technique in cytopathology will require further technical refinements to offset the advantages currently associated with image cytometry and immunocytochemistry.